Intelligent Energy-saving Control Method and System for Liquid-cooled Thermal Management System of Energy Storage Cabinet

Abstract

The present invention belongs to the field of thermal control energy saving of energy storage cabinets, and particularly relates to an intelligent energy saving control method and system for a liquid cooling type thermal management system of an energy storage cabinet, including: obtaining the working state data of the energy storage cabinet and the data of the thermal management system, and introducing an electrothermal conversion model and an energy saving cooling control model; secondly, accurately calculating the electrothermal conversion efficiency and the charge-discharge - temperature rise mapping space based on the working state data of the energy storage cabinet and the electrothermal conversion model; thirdly, inputting the charge-discharge - temperature rise mapping space and the data of the thermal management system into the energy saving cooling control model to generate the first collaborative control parameters and instructions; finally, feeding back the parameters and instructions to the collaborative control device, thereby accurately controlling the state change time point and the cooling output power of the liquid cooling unit, and adjusting the boost rate of the energy storage cabinet; the present invention effectively grasps the accurate time point of cooling through the accurate temperature rise and temperature drop process mapping space, improves the cooling efficiency, and reduces the cooling energy consumption.

Description

Technical Field

[0001] The present invention belongs to the field of thermal control energy saving of energy storage cabinets, and particularly relates to an intelligent energy saving control method and system for a liquid-cooled thermal management system of an energy storage cabinet. Background Art

[0002] During the operation of an energy storage cabinet, especially during the boost charge and discharge process, a large amount of heat is generated. If the heat cannot be effectively managed, the performance and lifespan of the energy storage cabinet will be affected. Most of the existing liquid-cooled thermal management systems for energy storage cabinets adopt relatively simple control methods, that is, by monitoring the overall temperature of the energy storage cabinet, and when the temperature reaches a preset critical point, the cooling measures are started. However, this method has obvious limitations. During the boost charge and discharge process of the energy storage cabinet, the heat generation situation of the battery is closely related to the charge and discharge rate. Different charge and discharge rates will result in different heat generation rates and heat quantities of the battery. However, the existing technology cannot dynamically adjust the cooling output efficiency and boost rate according to the charge and discharge rate, and cannot achieve two-way parallel adjustment of energy-saving cooling and boost process at a fixed charge and discharge rate, and cannot achieve faster adjustment of temperature anomalies and reduction of cooling energy consumption. Summary of the Invention

[0003] Aiming at the deficiencies of the existing technology, the present invention proposes an intelligent energy saving control method and system for a liquid-cooled thermal management system of an energy storage cabinet, including: obtaining the working state data of the energy storage cabinet and the data of the thermal management system, and introducing an electrothermal conversion model and an energy-saving cooling control model at the same time; secondly, based on the working state data of the energy storage cabinet and the electrothermal conversion model, accurately calculating the electrothermal conversion efficiency and the charge-discharge - temperature rise mapping space; thirdly, inputting the charge-discharge - temperature rise mapping space and the data of the thermal management system into the energy-saving cooling control model to generate the first collaborative control parameters and instructions; finally, feeding back the parameters and instructions to the collaborative control device to accurately control the state change time point and cooling output power of the liquid-cooled unit, and adjusting the boost rate of the energy storage cabinet; the present invention effectively grasps the accurate time point of cooling through the accurate temperature rise and cooling process mapping space, improves the cooling efficiency, and reduces the cooling energy consumption.

[0004] To achieve the above object, the present invention provides the following technical solutions:

[0005] An intelligent energy saving control method for a liquid-cooled thermal management system of an energy storage cabinet, including:

[0006] Obtaining the working state data of the energy storage cabinet, the data of the thermal management system, the electrothermal conversion model and the energy-saving cooling control model;

[0007] According to the working state data of the energy storage cabinet and the electrothermal conversion model, obtaining the electrothermal conversion efficiency corresponding to the current working state of the energy storage cabinet and the charge-discharge - temperature rise mapping space under the electrothermal conversion efficiency;

[0008] Input the charge-discharge - temperature rise mapping space under the electrothermal conversion efficiency and the data of the thermal management system into the energy-saving and cooling control model to generate the first collaborative control parameter and the first collaborative control instruction;

[0009] Feed back the first collaborative control parameter and the first collaborative control instruction to the collaborative control device for the energy storage cabinet and the control liquid cooling unit, control the time point of the change in the coolant circulation state in the liquid cooling unit and the cooling output power, and adjust the boosting rate of the energy storage cabinet.

[0010] Specifically, the working state data of the energy storage cabinet includes the charging or discharging voltage at the current moment, the total resistance of the energy storage cabinet and the target object, the charge or discharge amount per unit time, and the lower limit of the minimum voltage requirement for charging or discharging; the electrothermal conversion efficiency includes the charging electrothermal conversion efficiency and the discharging electrothermal conversion efficiency;

[0011] The steps for obtaining the charge-discharge - temperature rise mapping space under the electrothermal conversion efficiency include:

[0012] S101. Obtain the discharging voltage of the energy storage cabinet at the current moment, and obtain the charge amount released per unit time by the energy storage cabinet and the charge amount recharged per unit time by the target object according to the discharging voltage at the current moment;

[0013] S102. Obtain the discharging loss charge amount at the discharging voltage at the current moment according to the charge amount released per unit time by the energy storage cabinet and the charge amount recharged per unit time by the target object;

[0014] S103. Obtain the electrothermal conversion efficiency according to the discharging loss charge amount at the discharging voltage at the current moment and the total charge amount released by the energy storage cabinet per unit time;

[0015] S104. Pass the electrothermal conversion efficiency, the discharging voltage at the current moment, and the total resistance of the energy storage cabinet and the target object through the electrothermal conversion model to obtain the first electrothermal conversion energy consumption, the natural heat dissipation amount per unit time, and the effective electrothermal conversion amount for the temperature rise of the energy storage cabinet;

[0016] S105. Obtain the corresponding temperature rise change rate at the discharging voltage at the current moment according to the effective electrothermal conversion amount for the temperature rise of the energy storage cabinet.

[0017] Specifically, the steps for obtaining the charge-discharge - temperature rise mapping space under the electrothermal conversion efficiency further include:

[0018] S106. Construct a discharge - temperature rise mapping according to the discharging voltage at the current moment, the corresponding electrothermal conversion efficiency, and the temperature rise change rate, and repeat the processes of S101 - S105 to obtain the discharge - temperature rise mappings corresponding to different discharging voltages, and construct a discharge - temperature rise mapping space based on all the discharge - temperature rise mappings;

[0019] S107. Similarly, obtain the charging voltage of the energy storage cabinet at the current moment, and based on the same calculation process as in S101 - S106, obtain the charging - temperature rise mapping space.

[0020] Specifically, the data of the thermal management system includes: the output power of the liquid cooling unit, the circulation rate of the coolant, the configured path of the circulation pipeline, the normal operating temperature range of the energy storage cabinet battery pack, the coolant temperature and flow rate, the energy consumption data of the liquid cooling unit, and the position and heat dissipation distribution of the energy storage cabinet battery pack; the energy - saving and temperature - reduction control model includes a temperature - reduction optimization sub - model and a temperature - reduction control sub - model.

[0021] The steps for generating the first collaborative control parameter include:

[0022] Based on the obtained charging or discharging demand of the target object and the corresponding charging and discharging voltage at the current moment, obtain the predicted charging and discharging time length of the target object.

[0023] Based on the predicted charging and discharging time length of the target object and the normal operating temperature range of the energy storage cabinet battery pack, obtain the boosting rate corresponding to the normal temperature rise of the energy storage cabinet battery pack.

[0024] Based on the normal temperature - rise change rate of the energy storage cabinet battery pack and the predicted charging and discharging time length of the target object, obtain the normal boosting rate within the corresponding time period.

[0025] Based on the normal temperature - rise change rate and the actual temperature - rise change rate at the corresponding voltage in the charging - temperature rise mapping space, obtain the corresponding abnormal temperature - rise change rate value.

[0026] Specifically, the temperature - reduction optimization sub - model includes a first temperature - reduction optimization sub - path and a second optimization sub - path; the steps for generating the first collaborative control parameter include:

[0027] Based on the abnormal temperature - rise change rate value, the normal boosting rate, through the charging and discharging - temperature rise mapping space and the second optimization sub - path, obtain the first target boosting rate adjustment value.

[0028] Based on the abnormal temperature - rise change rate value, through the first temperature - reduction optimization sub - path, obtain the first target temperature - reduction optimization parameter set.

[0029] Specifically, the steps for obtaining the first target boosting rate adjustment value include:

[0030] Based on the abnormal temperature - rise change rate value at the current moment , obtain the abnormal heat energy release amount;

[0031] Based on the abnormal heat energy release amount, the lower limit of the minimum voltage demand for charging or discharging, and the mapping relationship corresponding to the charging and discharging - temperature rise mapping space, obtain the boosting rate corresponding to the current abnormal voltage.

[0032] Obtain the abnormal change rate of the boost rate based on the boost rate corresponding to the current abnormal voltage and the boost rate corresponding to the normal temperature rise of the energy storage cabinet battery pack.

[0033] Take the opposite of the abnormal change rate of the boost rate to obtain the first target boost rate adjustment value.

[0034] Specifically, the steps for obtaining the first target cooling optimization parameter set include:

[0035] Construct the first cooling mapping based on the configured path of the circulation pipeline, the distribution position of the energy storage cabinet battery pack, and the temperature rise change rate per unit time.

[0036] Based on the first cooling mapping, obtain the cooling target value corresponding to each circulation path.

[0037] Construct the second cooling mapping according to the cooling target value corresponding to each circulation path, the output power of the liquid cooling unit, the circulation rate of the coolant in each circulation pipeline, the coolant temperature, and the flow rate.

[0038] Based on the second cooling mapping, obtain the cooling rate corresponding to each circulation pipeline.

[0039] Construct the third cooling mapping according to the abnormal temperature rise change rate value, the cooling rate corresponding to each circulation pipeline, and the energy consumption data of the liquid cooling unit per unit time at the corresponding cooling rate.

[0040] Specifically, the steps for obtaining the first target cooling optimization parameter set further include:

[0041] Based on the third cooling mapping, minimize the cooling energy consumption of the liquid cooling unit and maximize the cooling rate to construct the cooling configuration optimization function in the first cooling optimization sub-path.

[0042] Use the maximum and minimum values of the circulation rate of the coolant in each circulation pipeline, the maximum cooling temperature value corresponding to the coolant, the maximum and minimum values of the flow rate, and the maximum and minimum values of the output power of the liquid cooling unit, and the upper limit of the cooling rate corresponding to the output power to construct the constraint conditions for the cooling configuration optimization function.

[0043] According to the cooling configuration optimization function and the constraint conditions configured in the second optimization sub-path, obtain the first target cooling optimization parameter set corresponding to the liquid cooling unit through the real-time abnormal temperature rise change rate value and the simulation algorithm.

[0044] Specifically, the temperature reduction control sub-model includes a first control sub-path and a second control sub-path; the first control sub-path corresponds to the first temperature reduction optimization sub-path; the second control sub-path corresponds to the second optimization sub-path; the first control sub-path generates a first liquid cooling control instruction according to the first target temperature reduction optimization parameter set obtained by the first temperature reduction optimization sub-path; the second control sub-path generates a second boost regulation instruction through the first target boost rate regulation value obtained by the second optimization sub-path;

[0045] When it is detected at the corresponding time point that the heating change rate and the boost rate are greater than the corresponding normal heating change rate and normal boost rate, the cooling output power and the boost rate of the energy storage cabinet are adjusted synchronously through the first liquid cooling control instruction and the second boost regulation instruction.

[0046] The intelligent energy-saving control system of the liquid-cooled thermal management system for the energy storage cabinet includes: a data acquisition module, a mapping module, an instruction generation module, and a regulation module;

[0047] The data acquisition module is used to acquire the working state data of the energy storage cabinet, the thermal management system data, the electro-thermal conversion model, and the energy-saving temperature reduction control model;

[0048] The mapping module is used to obtain the electro-thermal conversion efficiency corresponding to the current working state of the energy storage cabinet and the charge-discharge - heating mapping space under the electro-thermal conversion efficiency according to the working state data of the energy storage cabinet and the electro-thermal conversion model;

[0049] The instruction generation module is used to input the charge-discharge - heating mapping space under the electro-thermal conversion efficiency and the thermal management system data into the energy-saving temperature reduction control model to generate a first collaborative control parameter and a first collaborative control instruction;

[0050] The regulation module is used to feedback the first collaborative control parameter and the first collaborative control instruction to the collaborative control device of the energy storage cabinet and the control liquid cooling unit, and control the time point of the change in the coolant circulation state and the cooling output power in the liquid cooling unit and adjust the boost rate of the energy storage cabinet.

[0051] Compared with the prior art, the beneficial effects of the present invention are:

[0052] In view of the deficiencies of the prior art, the present invention obtains the working state data of the energy storage cabinet and the data of the thermal management system, constructs a charge-discharge - temperature rise mapping space in combination with the electro-thermal conversion model, realizes in-depth analysis of the working state of the energy storage cabinet, can accurately calculate the value of the abnormal temperature rise rate of electricity, and accordingly generates the first target boost rate adjustment value and the first target cooling optimization parameter set by using the cooling optimization sub-model in the energy-saving cooling control model. Then, the liquid cooling control instruction and the boost adjustment instruction are generated by the cooling control sub-model accordingly. Compared with the prior art, it can dynamically adjust the cooling output efficiency and the boost rate according to the charge-discharge rate, realize the two-way parallel adjustment of the energy-saving cooling and the boost process, can quickly adjust the abnormal temperature, effectively reduce the cooling energy consumption, and greatly improve the operation stability, safety and energy utilization efficiency of the energy storage cabinet. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 It is a flowchart of the intelligent energy-saving control method for the liquid-cooled thermal management system of the energy storage cabinet in Embodiment 1 of the present invention;

[0054] Figure 2 It is a module diagram of the intelligent energy-saving control system for the liquid-cooled thermal management system of the energy storage cabinet in Embodiment 2 of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0055] Embodiment 1

[0056] In the existing energy storage cabinet cooling technology, generally only the corresponding cooling process is adjusted, and the abnormal charge-discharge boost process is not adjusted. This results in the inability to solve the boost abnormality, and at the same time, a large amount of cooling energy consumption is required to adjust the heat released by abnormal charging in the early stage and the heat released by abnormal boosting in the current state, which greatly increases the cooling energy consumption. Therefore, please refer to Figure 1 , an embodiment provided by the present invention: an intelligent energy-saving control method for a liquid-cooled thermal management system of an energy storage cabinet, the steps include:

[0057] S1. Obtain the working state data of the energy storage cabinet, the data of the thermal management system, the electro-thermal conversion model and the energy-saving cooling control model;

[0058] Further, the working state data of the energy storage cabinet in this embodiment includes the charging or discharging voltage at the current moment, the total resistance of the energy storage cabinet and the target object, the charging or discharging charge amount per unit time, and the lower limit of the minimum voltage requirement for charging or discharging;

[0059] The data of the thermal management system includes: the output power of the liquid cooling unit, the circulation rate of the coolant, the configuration path of the circulation pipeline, the normal working temperature range of the energy storage cabinet battery pack, the coolant temperature and flow rate, the energy consumption data of the liquid cooling unit, and the position and heat dissipation distribution of the energy storage cabinet battery pack.

[0060] S2. Obtain the electrothermal conversion efficiency corresponding to the current working state of the energy storage cabinet and the charge-discharge - temperature rise mapping space at the electrothermal conversion efficiency according to the working state data of the energy storage cabinet and the electrothermal conversion model.

[0061] Furthermore, the electrothermal conversion efficiency in this embodiment includes the charging electrothermal conversion efficiency and the discharging electrothermal conversion efficiency.

[0062] The steps for obtaining the charge-discharge - temperature rise mapping space at the electrothermal conversion efficiency include:

[0063] S101. Obtain the discharging voltage of the energy storage cabinet at the current moment, and obtain the charge released by the energy storage cabinet per unit time and the charge charged to the target per unit time according to the discharging voltage at the current moment.

[0064] S102. Obtain the discharging loss charge at the discharging voltage at the current moment according to the charge released by the energy storage cabinet per unit time and the charge charged to the target per unit time.

[0065] S103. Obtain the electrothermal conversion efficiency according to the discharging loss charge at the discharging voltage at the current moment and the total charge released by the energy storage cabinet per unit time.

[0066] S104. Pass the electrothermal conversion efficiency, the discharging voltage at the current moment, and the total resistance of the energy storage cabinet and the target through the electrothermal conversion model to obtain the first electrothermal conversion energy consumption, the natural heat dissipation per unit time, and the effective electrothermal conversion amount for the temperature rise of the energy storage cabinet.

[0067] Furthermore, the first electrothermal conversion energy consumption in this embodiment is calculated through the work formula, and the natural heat dissipation is calculated through the Stefan - Boltzmann law to obtain the heat dissipated into the air during the charge-discharge voltage boost process of the energy storage cabinet.

[0068] S105. Obtain the corresponding temperature rise change rate at the current discharging voltage according to the effective electrothermal conversion amount for the temperature rise of the energy storage cabinet.

[0069] S106. Construct a discharge - temperature rise mapping according to the discharging voltage at the current moment, the corresponding electrothermal conversion efficiency, and the temperature rise change rate, and repeat the process of S101 - S105 to obtain the discharge - temperature rise mappings corresponding to different discharging voltages, and construct a discharge - temperature rise mapping space based on all the discharge - temperature rise mappings.

[0070] S107. Similarly, obtain the charging voltage of the energy storage cabinet at the current moment, and obtain a charge - temperature rise mapping space based on the same calculation process of S101 - S106.

[0071] Furthermore, the discharge - temperature rise mapping and the charge - temperature rise mapping in this embodiment are obtained by fitting using the kernel function of the support vector machine through the discharging voltage at the current moment, the corresponding electrothermal conversion efficiency, and the temperature rise change rate.

[0072] Furthermore, the charge-discharge - temperature rise mapping space in this embodiment includes a discharge - temperature rise mapping space and a charge - temperature rise mapping space.

[0073] This process comprehensively collects the working state data of the energy storage cabinet and the data of the thermal management system, laying a solid foundation for in - depth analysis of the operation status of the energy storage cabinet; when obtaining the electro - thermal conversion efficiency and the charge - discharge - temperature rise mapping space corresponding to the current working state of the energy storage cabinet, through layer - by - layer analysis and calculation of data such as discharge voltage and charge quantity, the electro - thermal conversion efficiency, energy consumption heat, and temperature rise change rate are obtained, accurately quantifying the energy conversion and temperature change during the discharge process of the energy storage cabinet; and by expanding to different voltage conditions to construct the mapping space, it can more comprehensively reflect the electro - thermal characteristics of the energy storage cabinet under various charge - discharge conditions, which enables the system to accurately master the heat generation trend of the energy storage cabinet under different working states, providing an accurate basis for dynamically adjusting the cooling strategy and boost rate according to the charge - discharge rate subsequently. Based on this, in the face of different charge - discharge scenarios, more efficient temperature control can be achieved, avoiding performance degradation and safety hazards caused by abnormal temperature, while helping to optimize energy utilization, reducing unnecessary energy consumption, and improving the overall operation efficiency and stability of the energy storage cabinet.

[0074] S3. Input the charge - discharge - temperature rise mapping space under the electro - thermal conversion efficiency and the thermal management system data into the energy - saving cooling control model to generate a first collaborative control parameter and a first collaborative control instruction;

[0075] Furthermore, the energy - saving cooling control model in this embodiment includes a cooling optimization sub - model and a cooling control sub - model; furthermore, the cooling optimization sub - model is constructed by a BP neural network optimized by a genetic algorithm, and two optimization paths are built in this model, namely a first cooling optimization sub - path and a second optimization sub - path. When performing optimization operations, the first cooling optimization sub - path and the second optimization sub - path simultaneously perform parameter optimization operations and simultaneously output the corresponding first target boost rate adjustment value and the first target cooling optimization parameter set, enabling the overall sub - model to achieve a synchronous two - way optimization process.

[0076] Furthermore, the steps for generating the first collaborative control parameter in this embodiment include:

[0077] Obtain the predicted charge - discharge time length of the target object according to the obtained charge - discharge demand of the target object to be charged or discharged and the corresponding charge - discharge voltage at the current moment;

[0078] Obtain the boost rate corresponding to the normal temperature rise of the energy storage cabinet battery pack according to the predicted charge - discharge time length of the target object and the normal working temperature range of the energy storage cabinet battery pack;

[0079] Obtain the normal boost rate within the corresponding time period according to the normal temperature rise change rate of the energy storage cabinet battery pack and the predicted charge - discharge time length of the target object;

[0080] According to the normal temperature rise change rate and the actual temperature rise change rate at the corresponding voltage in the charge-discharge - temperature rise mapping space, the corresponding abnormal temperature rise change rate value is obtained.

[0081] Furthermore, in this operation process, by adjusting the corresponding abnormal boost rate while ensuring the minimum lower limit requirements of charge and discharge, it can not only meet the charge and discharge requirements but also minimize the abnormal boost rate to ensure the safety of the charging device.

[0082] Furthermore, the temperature reduction optimization sub-model in this embodiment includes a first temperature reduction optimization sub-path and a second optimization sub-path; the steps for generating the first collaborative control parameter include:

[0083] According to the abnormal temperature rise change rate value, the normal boost rate, through the charge-discharge - temperature rise mapping space and the second optimization sub-path, the first target boost rate adjustment value is obtained;

[0084] Furthermore, the steps for obtaining the first target boost rate adjustment value in this embodiment include:

[0085] According to the abnormal temperature rise change rate value at the current moment , the abnormal heat energy release amount is obtained;

[0086] According to the abnormal heat energy release amount, the lower limit of the minimum voltage requirement for charge or discharge, and the mapping relationship corresponding to the charge-discharge - temperature rise mapping space, the boost rate corresponding to the current abnormal voltage is obtained;

[0087] According to the boost rate corresponding to the current abnormal voltage and the boost rate corresponding to the normal temperature rise of the energy storage cabinet battery pack, the abnormal change rate of the boost rate is obtained;

[0088] Taking the opposite number of the abnormal change rate of the boost rate, the first target boost rate adjustment value is obtained.

[0089] According to the abnormal temperature rise change rate value, through the first temperature reduction optimization sub-path, the first target temperature reduction optimization parameter set is obtained.

[0090] Based on the charging or discharging demand of the target object and the corresponding charging and discharging voltage at the current moment, the predicted charging and discharging time length of the target object is calculated, which provides a basis in the time dimension for subsequent analysis of the thermal behavior of the battery during the entire charging and discharging process. Combining with the normal operating temperature range of the battery pack, the boosting rate corresponding to the normal temperature rise of the battery pack is obtained, and this rate is a key indicator to ensure the normal operation of the battery. Then, by comparing the normal temperature rise change rate with the actual temperature rise change rate at the corresponding voltage in the charging-temperature rise mapping space, the abnormal temperature rise change rate value is obtained. Based on this abnormal value, the abnormal heat energy release amount is calculated, and then, according to the mapping relationship of the charging-discharging-temperature rise mapping space, the boosting rate corresponding to the current abnormal voltage is determined. Comparing it with the normal boosting rate, the abnormal change rate of the boosting rate is obtained, and the opposite number is taken as the first target boosting rate adjustment value. This adjustment method is based on the normal operating state of the battery and makes a reverse adjustment for the abnormal temperature rise situation, so that the boosting rate returns to a safe and reasonable range, thereby reducing the damage to the equipment caused by abnormal boosting; through the above process, the boosting rate can be accurately calculated and adjusted, while meeting the charging and discharging requirements, minimizing the abnormal boosting rate to the greatest extent, which is crucial for ensuring the safety of the charging equipment. Abnormal boosting may lead to out-of-control internal chemical reactions of the battery, accelerate battery aging, and even cause safety accidents such as overheating and fire. By reasonably adjusting the boosting rate, the service life of the battery can be effectively extended, and the long-term stable operation of the energy storage system can be ensured.

[0091] Furthermore, the steps for obtaining the first target temperature reduction optimization parameter set in this embodiment include:

[0092] Construct a first temperature reduction mapping according to the configured path of the circulation pipeline, the distribution position of the battery pack in the energy storage cabinet, and the temperature rise change rate per unit time;

[0093] Furthermore, in this embodiment, by establishing the first cooling mapping, the corresponding relationship between the position points where the battery pack needs to be cooled, the corresponding cooling requirements, and the corresponding cooling pipelines can be clearly described. This enables the subsequent cooling operation to accurately locate the area with actual abnormal heat generation in the battery pack, avoiding the problems of energy waste and low cooling efficiency caused by blind cooling. For example, when a certain corner of the energy storage cabinet heats up rapidly due to the battery pack layout or local charge and discharge abnormalities, the first cooling mapping can clearly indicate this position and match the corresponding cooling pipeline to ensure that the coolant can preferentially and effectively cool this area. Further, during the actual operation process, the battery packs in the energy storage cabinet do not generate heat evenly. Due to factors such as charge and discharge states and heat conduction with other components, the temperature rise of batteries at different positions varies. The configured path of the circulation pipeline determines the positions that the coolant can reach. Combining with the temperature rise change rate per unit time, the urgency of the cooling requirement for each position can be determined. For example, in a certain area, if the temperature rise change rate is high, it indicates that this area requires stronger cooling. Then, in the first cooling mapping, this area will be associated with the pipeline that can provide a greater cooling capacity. In this way, the first cooling mapping constructed based on the corresponding relationship between spatial position and heat demand provides a basic framework for subsequent precise cooling.

[0094] Based on the first cooling mapping, obtain the cooling target value corresponding to each circulation path;

[0095] According to the cooling target value corresponding to each circulation path, the output power of the liquid cooling unit, the circulation rate of the coolant in each circulation pipeline, the coolant temperature, and the flow rate, construct the second cooling mapping;

[0096] Further, in this embodiment, the cooling target value corresponding to each cycle path obtained based on the first cooling mapping, and the second cooling mapping combines parameters such as the output power of the liquid cooling unit, the circulation rate of the coolant in each circulation pipeline, the coolant temperature, and the flow rate, further refining the cooling capacity that each cycle path can actually achieve. This helps to reasonably plan the cooling tasks of each cycle path according to the actual cooling resource configuration, and avoid poor overall cooling effect caused by excessive expectations for a certain cycle path. For example, some circulation pipelines may have limited actual cooling capacity due to coolant flow restrictions or pipeline aging, and the second cooling mapping can accurately reflect these situations, thereby adjusting the cooling strategy. In practical applications, the output power of the liquid cooling unit determines its total refrigeration capacity, and parameters such as the circulation rate, temperature, and flow rate of the coolant directly affect the heat dissipation effect of each circulation pipeline. Taking the coolant flow rate as an example, the larger the flow rate, the more heat is carried away per unit time, but it may also be limited by the output power of the liquid cooling unit. According to the law of conservation of energy, the heat absorbed by the coolant is equal to its specific heat capacity multiplied by the mass flow rate multiplied by the temperature change. By combining the cooling target value in the first cooling mapping with these actual cooling parameters, the cooling rate that each cycle path can achieve under the existing conditions can be calculated, thereby constructing the second cooling mapping to achieve the matching of the theoretical cooling demand and the actual cooling capacity.

[0097] Based on the second cooling mapping, obtain the cooling rate corresponding to each circulation pipeline;

[0098] According to the abnormal temperature rise change rate value, the cooling rate corresponding to each circulation pipeline, and the energy consumption data of the liquid cooling unit per unit time at the corresponding cooling rate, construct the third cooling mapping.

[0099] Based on the third cooling mapping, minimize the cooling energy consumption of the liquid cooling unit and maximize the cooling rate, and construct the cooling configuration optimization function in the first cooling optimization sub-path;

[0100] Further, in this embodiment, the third cooling mapping comprehensively considers the abnormal temperature rise change rate value, the cooling rate corresponding to each circulation pipeline, and the energy consumption data of the liquid cooling unit per unit time at the corresponding cooling rate in the first cooling mapping and the second cooling mapping. Its establishment provides comprehensive data support for subsequent optimization of the cooling strategy, and can minimize the energy consumption on the premise of ensuring the cooling effect at the corresponding time point. By analyzing the energy consumption at different cooling rates, the balance point between energy consumption and cooling effect can be found, and the energy consumption waste caused by excessive cooling can be avoided. For example, in some cases, increasing the cooling rate may significantly increase the energy consumption, but the improvement of the overall cooling effect is not obvious. The third cooling mapping can help identify this situation, thereby optimizing the cooling plan.

[0101] Construct the constraint conditions corresponding to the cooling configuration optimization function by using the maximum and minimum values of the coolant circulation rate in each loop pipeline, the maximum temperature drop value corresponding to the coolant, the maximum and minimum values of the flow rate, and the maximum and minimum values of the output power of the liquid cooling unit, as well as the upper limit value of the cooling rate corresponding to the output power.

[0102] According to the cooling configuration optimization function and constraint conditions configured by the second optimization sub-path, obtain the first target cooling optimization parameter set corresponding to the liquid cooling unit through the real-time abnormal temperature rise change rate value and the simulation algorithm.

[0103] Furthermore, in this embodiment, the above parameter setting constraint conditions are because the coolant circulation rate is restricted by pipeline characteristics, pump performance, and system safety. If the circulation rate is too low, the expected cooling effect may not be achieved; if it is too high, it may cause excessive pipeline pressure, increasing the risk of system wear and failure. Therefore, setting the upper and lower limits of the circulation rate is to ensure system safety.

[0104] According to the physical properties of the coolant, such as ethylene glycol aqueous solution, its specific heat capacity and boiling point, etc., there is a reasonable maximum acceptable temperature reduction range. Beyond this range, the coolant may lose its cooling capacity and even damage the pipeline or other equipment. Therefore, the cooling temperature of the coolant is restricted by this limit value, and a reasonable coolant circulation rate and flow rate are designed according to this cooling temperature, so that the temperature of the coolant applied to cooling is maintained at the optimal cooling temperature, that is, the heat is absorbed fastest and the most heat, and the coolant that has absorbed the heat is quickly replaced through the designed reasonable circulation rate.

[0105] The output power of the liquid cooling unit is restricted by the design and electrical and mechanical performance of the unit itself. Exceeding the power range may cause the unit to overheat, reduce efficiency or be damaged. In addition, a reasonable output can not only achieve the cooling effect but also reduce unnecessary energy consumption output. The optimal output power obtained by solving through the constraint conditions and the cooling configuration optimization function can greatly reduce energy consumption.

[0106] Furthermore, the cooling control sub-model in this embodiment includes a first control sub-path and a second control sub-path; the first control sub-path corresponds to the first cooling optimization sub-path; the second control sub-path corresponds to the second optimization sub-path; the first control sub-path generates a first liquid cooling control instruction according to the first target cooling optimization parameter set obtained by the first cooling optimization sub-path; the second control sub-path generates a second boost adjustment instruction through the first target boost rate adjustment value obtained by the second optimization sub-path.

[0107] When the detected heating rate of temperature rise and boosting rate are greater than the corresponding normal heating rate of temperature rise and normal boosting rate at the corresponding time points, the cooling output power and the boosting rate of the energy storage cabinet are adjusted synchronously through the first liquid cooling control instruction and the second boosting adjustment instruction; further, in this embodiment, the cooling control sub-model is constructed by a pre-trained reinforcement learning algorithm.

[0108] The energy-saving cooling control model of this process generates collaborative control parameters and instructions based on the charge-discharge - temperature rise mapping space and the data of the thermal management system to achieve precise control of the energy storage cabinet. By calculating the predicted charge-discharge time length of the target object and combining the normal operating temperature range of the battery pack, the normal boosting rate and the normal heating rate of temperature rise can be accurately obtained. Comparing it with the actual heating rate of temperature rise, the abnormal heating rate of temperature rise value can be obtained, providing the core basis for subsequent targeted adjustment; for example, when it is detected that the boosting rate and the heating rate of temperature rise exceed the normal range, the system can respond quickly and accurately calculate the first target boosting rate adjustment value through the second optimization sub-path. In this process, based on the abnormal heat release amount and the charge-discharge - temperature rise mapping relationship, the boosting rate corresponding to the abnormal voltage is determined, and then the abnormal change rate of the boosting rate is obtained. Finally, taking the opposite number as the adjustment value can quickly adjust the boosting rate to a reasonable range and maintain the stable operation of the energy storage cabinet; secondly, in the process of generating the first target cooling optimization parameter set, by constructing multiple cooling mappings, factors such as the circulating pipeline configuration, the distribution of the battery pack, and the operating parameters of the liquid cooling unit are fully considered to achieve refined management of the cooling energy consumption. From the first cooling mapping to the third cooling mapping, the cooling requirements, the relationship between the cooling rate and the energy consumption of each circulating path are analyzed step by step. On this basis, a cooling configuration optimization function is constructed with the goal of minimizing the cooling energy consumption of the liquid cooling unit and maximizing the cooling rate, and combining the maximum and minimum values of parameters such as the coolant circulation rate, temperature, flow rate, and the output power of the liquid cooling unit as constraints, and using the simulation algorithm to obtain the optimal cooling parameter set. This process enables the liquid cooling unit to minimize the energy consumption while meeting the cooling requirements, improve the energy utilization efficiency. In addition, the cooling output power and the boosting rate of the energy storage cabinet are adjusted synchronously to effectively protect the energy storage cabinet and related equipment. When an abnormal situation is monitored, the first control sub-path generates the first liquid cooling control instruction according to the first target cooling optimization parameter set, and timely adjusts the working state of the liquid cooling unit to ensure that the temperature of the energy storage cabinet is maintained within the normal operating temperature range, preventing the battery performance from degrading, the service life from shortening, or even causing safety accidents due to overheating. The second control sub-path generates the second boosting adjustment instruction through the first target boosting rate adjustment value, reasonably controls the boosting rate, avoids damage to the energy storage cabinet caused by abnormal voltage changes, and reduces the heat output while meeting the charge-discharge requirements. This collaborative control mechanism comprehensively protects the energy storage cabinet equipment and improves the reliability and stability of the system.

[0109] S4. Feed the first collaborative control parameter and the first collaborative control instruction back to the collaborative control device of the energy storage cabinet and the control liquid cooling unit, control the time point of the change in the coolant circulation state in the liquid cooling unit and the cooling output power, and adjust the boosting rate of the energy storage cabinet, so that the energy storage cabinet and the thermal management system operate in the lowest energy consumption state while meeting the charging and discharging requirements.

[0110] Further, in this embodiment, input the first liquid cooling control instruction into the corresponding control device of the liquid cooling unit to control the output power of the corresponding liquid cooling unit, the coolant flow rate and velocity of the corresponding circulation path, so that the cooling rate on the corresponding circulation path is always greater than the battery pack temperature rise change rate caused by charging and discharging. At the same time, input the second boosting adjustment instruction into the charge and discharge control device to adjust the abnormal boosting rate during charge and discharge.

[0111] Embodiment 2

[0112] Please refer to Figure 2 , another embodiment provided by the present invention: an intelligent energy-saving control system for a liquid-cooled thermal management system of an energy storage cabinet, including: a data acquisition module, a mapping module, an instruction generation module, and a regulation module;

[0113] The data acquisition module is used to acquire the working state data of the energy storage cabinet, the thermal management system data, the electrothermal conversion model, and the energy-saving cooling control model;

[0114] The mapping module is used to obtain the electrothermal conversion efficiency corresponding to the current working state of the energy storage cabinet and the charge-discharge - temperature rise mapping space under the electrothermal conversion efficiency according to the working state data of the energy storage cabinet and the electrothermal conversion model;

[0115] The instruction generation module is used to input the charge-discharge - temperature rise mapping space under the electrothermal conversion efficiency and the thermal management system data into the energy-saving cooling control model to generate the first collaborative control parameter and the first collaborative control instruction;

[0116] The regulation module is used to feed the first collaborative control parameter and the first collaborative control instruction back to the collaborative control device of the energy storage cabinet and the control liquid cooling unit, control the time point of the change in the coolant circulation state in the liquid cooling unit and the cooling output power, and adjust the boosting rate of the energy storage cabinet.

[0117] The embodiments of the present invention have been described above in conjunction with the accompanying drawings. However, the present invention is not limited to the above specific embodiments. The above specific embodiments are merely illustrative and not restrictive. Those of ordinary skill in the art, under the inspiration of the present invention, without departing from the spirit and scope protected by the claims of the present invention, can also make changes, modifications, substitutions, and variations to the above embodiments, and these all fall within the protection scope of the present invention.

Claims

1. An intelligent energy-saving control method for a liquid-cooled thermal management system of an energy storage cabinet, characterized in that: include: Obtain energy storage cabinet working status data, thermal management system data and energy-saving and cooling control model; The energy-saving and cooling control model includes a cooling optimization sub-model and a cooling control sub-model; The cooling optimization sub-model includes a first cooling optimization sub-path and a second optimization sub-path; the cooling control sub-model includes a first control sub-path and a second control sub-path; According to the working state data of the energy storage cabinet, the electric-thermal conversion efficiency corresponding to the current working state of the energy storage cabinet and the charge-discharge-temperature rise mapping space under the electric-thermal conversion efficiency are obtained; Inputting the charge-discharge-temperature rise mapping space under the electric-thermal conversion efficiency and the thermal management system data into the energy-saving and temperature-reducing control model to generate the first coordinated control parameter and the first coordinated control instruction; Feeding back the first coordinated control parameter and the first coordinated control instruction to the coordinated control device for controlling the energy storage cabinet and the liquid cooling unit, controlling the time point of the coolant circulation state change in the liquid cooling unit and the cooling output power and adjusting the boost rate of the energy storage cabinet; The energy storage cabinet working status data includes the current charging or discharging voltage, the total resistance of the energy storage cabinet and the target object, the charging or discharging charge per unit time, and the lower limit of the minimum voltage requirement for charging or discharging; the electric heat conversion efficiency includes the charging heat conversion efficiency and the discharging heat conversion efficiency; The step of acquiring the charge-discharge-temperature rise mapping space under the electrothermal conversion efficiency comprises: S101, obtaining the current discharge voltage of the energy storage cabinet, and obtaining the amount of charge released per unit time by the energy storage cabinet and the amount of charge charged per unit time by the target object according to the current discharge voltage; S102, obtaining the discharge loss charge under the discharge voltage at the current moment according to the charge released per unit time by the energy storage cabinet and the charge charged per unit time by the target object; S103, obtaining the electric-to-heat conversion efficiency according to the discharge loss charge at the current discharge voltage and the total charge released by the energy storage cabinet per unit time; S104, based on the electrothermal conversion efficiency, the discharge voltage at the current moment, and the total resistance of the energy storage cabinet and the target object, obtain the first electrothermal conversion energy consumption and the natural heat dissipation in the corresponding unit time and the effective temperature rise electrothermal conversion amount of the energy storage cabinet; S105, obtaining the temperature rise change rate corresponding to the discharge voltage at the current moment according to the effective temperature rise electric heat conversion amount of the energy storage cabinet; The first electrothermal conversion energy consumption is calculated by a work formula, and the natural heat dissipation is calculated by the Stefan-Boltzmann law to obtain the heat dissipated into the air by the energy storage cabinet during the charging, discharging and voltage boosting process; The workflow of the energy-saving and cooling control model includes: The charge-discharge-temperature rise mapping space and the thermal management system data are input into the BP neural network model optimized by the genetic algorithm, and synchronous bidirectional optimization is performed through the built-in first cooling optimization sub-path and the second optimization sub-path. Among them, the first path constructs the first cooling map based on the circulation pipeline configuration, battery pack distribution and temperature rise change rate to locate the corresponding relationship between the heating area and the pipeline; the second cooling map is generated in combination with the output power of the liquid cooling unit and the coolant parameters to quantify the actual cooling capacity of each pipeline; the third cooling map is constructed according to the abnormal temperature rise change rate and energy consumption data, and the first target cooling optimization parameter set under the constraint conditions is solved by the simulation algorithm with the minimum energy consumption and the maximum cooling rate as the target. The second path calculates the abnormal heat energy release, combines the charge-discharge-temperature rise mapping relationship to reverse the abnormal boost rate, and compares it with the normal value and takes the opposite number to generate the first target boost rate adjustment value. Finally, the control sub-model driven by reinforcement learning combines the first target cooling optimization parameter set and the first target boost rate adjustment value to synchronously output the first liquid cooling control instruction and the second boost adjustment instruction to achieve dynamic coordinated control of the cooling and boost rates.

2. The intelligent energy-saving control method for the liquid-cooled thermal management system of the energy storage cabinet according to claim 1, characterized in that: The step of acquiring the charge-discharge-temperature rise mapping space also includes: S106, constructing a discharge-temperature rise mapping according to the current discharge voltage and the corresponding electrothermal conversion efficiency and temperature rise change rate, and repeating the processes S101-S105 to obtain discharge-temperature rise mappings corresponding to different discharge voltages, and constructing a discharge-temperature rise mapping space based on all discharge-temperature rise mappings; S107. Similarly, the current charging voltage of the energy storage cabinet is obtained, and based on the same calculation process of S101-S106, the charging-temperature rise mapping space is obtained.

3. The intelligent energy-saving control method for the liquid-cooled thermal management system of the energy storage cabinet according to claim 2, characterized in that: The thermal management system data includes: the output power of the liquid cooling unit, the circulation rate of the coolant, the configuration path of the circulation pipeline, the normal operating temperature range of the energy storage cabinet battery pack, the coolant temperature and flow rate, the energy consumption data of the liquid cooling unit, and the location and heat dissipation distribution of the energy storage cabinet battery pack; The step of generating the first collaborative control parameter comprises: Obtain the predicted charging and discharging time length of the target object according to the acquired charging or discharging demand of the target object and the corresponding charging and discharging voltage at the current moment; According to the predicted charging and discharging time length of the target object and the normal operating temperature range of the energy storage cabinet battery pack, the voltage increase rate corresponding to the normal temperature rise of the energy storage cabinet battery pack is obtained; According to the normal temperature rise rate of the energy storage cabinet battery pack and the predicted charging and discharging time length of the target object, the normal voltage rise rate in the corresponding time period is obtained; According to the normal temperature rise change rate and the actual temperature rise change rate at the corresponding voltage in the charging-temperature rise mapping space, the corresponding abnormal temperature rise change rate value is obtained.

4. The intelligent energy-saving control method for the liquid-cooled thermal management system of the energy storage cabinet according to claim 3, characterized in that: The step of generating the first collaborative control parameter comprises: According to the abnormal temperature rise change rate value, the normal voltage rise rate, the charge-discharge-temperature rise mapping space and the second optimization sub-path, a first target voltage rise rate adjustment value is obtained; According to the abnormal temperature rise change rate value, a first target temperature reduction optimization parameter set is obtained through a first temperature reduction optimization subpath.

5. The intelligent energy-saving control method for the liquid-cooled thermal management system of the energy storage cabinet according to claim 4, characterized in that: The step of obtaining the first target boost rate adjustment value comprises: According to the abnormal temperature change rate value at the current moment , obtain the abnormal heat energy release; According to the mapping relationship between the abnormal heat energy release, the lower limit of the minimum voltage requirement for charging or discharging, and the charge-discharge-temperature rise mapping space, the voltage boost rate corresponding to the current abnormal voltage is obtained; Obtaining an abnormal change rate of the voltage boost rate according to the voltage boost rate corresponding to the current abnormal voltage and the voltage boost rate corresponding to the normal temperature rise of the battery pack of the energy storage cabinet; The inverse of the abnormal change rate of the boost rate is taken to obtain a first target boost rate adjustment value.

6. The intelligent energy-saving control method for the liquid-cooled thermal management system of the energy storage cabinet according to claim 5, characterized in that: The step of obtaining the first target cooling optimization parameter set comprises: Constructing a first cooling map according to the configuration path of the circulation pipeline, the distribution position of the battery group of the energy storage cabinet, and the temperature rise change rate per unit time; Based on the first cooling map, obtaining a cooling target value corresponding to each circulation path; Constructing a second cooling map according to the cooling target value corresponding to each circulation path, the output power of the liquid cooling unit, and the circulation rate, cooling liquid temperature and flow rate of the cooling liquid in each circulation pipeline; Based on the second temperature reduction map, obtaining a temperature reduction rate corresponding to each circulation pipeline; The third cooling map is constructed according to the abnormal temperature rise change rate value, the cooling rate corresponding to each circulation pipeline, and the energy consumption data of the liquid cooling unit per unit time at the corresponding cooling rate.

7. The intelligent energy-saving control method for the liquid-cooled thermal management system of the energy storage cabinet according to claim 6, characterized in that: The step of obtaining the first target cooling optimization parameter set further includes: Based on the third cooling mapping, the cooling energy consumption of the liquid cooling unit is minimized and the cooling rate is maximized, so as to construct a cooling configuration optimization function in the first cooling optimization subpath; By using the maximum and minimum values ​​of the circulation rate of the coolant in each circulation pipeline, the maximum cooling temperature value and the maximum and minimum values ​​of the flow rate corresponding to the coolant, the maximum and minimum values ​​of the output power of the liquid cooling unit, and the upper limit value of the cooling rate corresponding to the output power, the constraint conditions corresponding to the cooling configuration optimization function are constructed; According to the cooling configuration optimization function and constraint conditions configured in the second optimization subpath, the first target cooling optimization parameter set corresponding to the liquid cooling unit is obtained through the real-time abnormal temperature rise change rate value and the simulation algorithm.

8. The intelligent energy-saving control method for the liquid-cooled thermal management system of the energy storage cabinet according to claim 7, characterized in that: The first control subpath corresponds to the first cooling optimization subpath; the second control subpath corresponds to the second optimization subpath; the first control subpath generates a first liquid cooling control instruction according to the first target cooling optimization parameter set obtained by the first cooling optimization subpath; The second control subpath generates a second boost adjustment instruction through the first target boost rate adjustment value obtained by the second optimization subpath; When it is detected at a corresponding time point that the temperature rise change rate and the voltage boost rate are greater than the corresponding normal temperature rise change rate and the normal voltage boost rate, the cooling output power and the energy storage cabinet voltage boost rate are adjusted synchronously through the first liquid cooling control instruction and the second voltage boost adjustment instruction.

9. An intelligent energy-saving control system for a liquid-cooled thermal management system of an energy storage cabinet, which is used to implement an intelligent energy-saving control method for a liquid-cooled thermal management system of an energy storage cabinet according to any one of claims 1 to 8, characterized in that: include: Data acquisition module, mapping module, instruction generation module and control module; The data acquisition module is used to acquire the working status data of the energy storage cabinet, the thermal management system data and the energy-saving and cooling control model; The mapping module is used to obtain the electric-thermal conversion efficiency corresponding to the current working state of the energy storage cabinet and the charge-discharge-temperature rise mapping space under the electric-thermal conversion efficiency according to the working state data of the energy storage cabinet; The instruction generation module is used to input the charge-discharge-temperature rise mapping space under the electric-heat conversion efficiency and the thermal management system data into the energy-saving and cooling control model to generate the first coordinated control parameter and the first coordinated control instruction; The control module is used to feed back the first collaborative control parameter and the first collaborative control instruction to the energy storage cabinet and the collaborative control device for controlling the liquid cooling unit, control the time point of the coolant circulation state change in the liquid cooling unit and the cooling output power, and adjust the boost rate of the energy storage cabinet.

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