Intelligent energy-saving control method and system of energy storage cabinet liquid-cooled thermal management system

Abstract

The invention belongs to the field of thermal control and energy conservation 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, and the method comprises the steps: 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 an electrothermal conversion model, accurately calculating electrothermal conversion efficiency and a charge-discharge-temperature rise mapping space; thirdly, inputting data of the charging and discharging-heating mapping space and the thermal management system into the energy-saving cooling control model, and generating a first cooperative control parameter and instruction; and finally, parameters and instructions are fed back to a cooperative control device, so that the state change time point and the cooling output power of the liquid cooling unit are accurately controlled, and the boosting rate of the energy storage cabinet is adjusted. According to the method, the accurate time point of cooling is effectively mastered through the accurate mapping space of the heating and cooling processes, the cooling efficiency is improved, and the cooling energy consumption is reduced.

Description

Technical Field

[0001] The present invention belongs to the field of thermal control and energy saving of energy storage cabinets, and in particular 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 the energy storage cabinet, especially during the boost charging and discharging process, a large amount of heat will be generated. If the heat cannot be effectively managed, the performance and life of the energy storage cabinet will be affected. Most of the existing liquid-cooled thermal management systems for energy storage cabinets adopt a relatively simple control method, that is, by monitoring the overall temperature of the energy storage cabinet, when the temperature reaches the preset critical point, the cooling measures are started; however, this method has obvious limitations. During the boost charging and discharging process of the energy storage cabinet, the heating of the battery is closely related to the charging and discharging rate. Different charging and discharging rates will lead to different heating rates and heat generation of the battery. However, the existing technology cannot dynamically adjust the cooling output efficiency and boost rate according to the charging and discharging rate, and cannot realize the timed bidirectional parallel adjustment of the energy-saving cooling and boosting process under the charging and discharging rate, so as to achieve faster regulation of temperature abnormalities and reduction of cooling energy consumption. Summary of the invention

[0003] In view of the deficiencies in the prior art, the present invention proposes an intelligent energy-saving control method and system for a liquid-cooled thermal management system of an energy storage cabinet, comprising: acquiring the working state data of the energy storage cabinet and the thermal management system data, and simultaneously introducing an electric-thermal conversion model and an energy-saving and cooling control model; secondly, based on the working state data of the energy storage cabinet and the electric-thermal conversion model, accurately calculating the electric-thermal conversion efficiency and the charge-discharge-temperature rise mapping space; thirdly, inputting the charge-discharge-temperature rise mapping space and the thermal management system data into the energy-saving and cooling control model to generate the first collaborative control parameters and instructions; finally, feeding back the parameters and instructions to the collaborative control device, so as to accurately control the state change time point and the cooling output power of the liquid cooling unit, and adjust the boost rate of the energy storage cabinet; the present invention effectively grasps the exact time point of cooling through the precise mapping space of the heating and cooling process, improves the cooling efficiency, and reduces the cooling energy consumption.

[0004] To achieve the above object, the present invention provides the following technical solutions: An intelligent energy-saving control method for a liquid-cooled thermal management system of an energy storage cabinet includes: Obtain energy storage cabinet working status data, thermal management system data, electric heat conversion model and energy-saving and cooling control model; According to the working state data of the energy storage cabinet and the electrothermal conversion model, 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 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; The first collaborative control parameter and the first collaborative control instruction are fed back to the collaborative control device of the energy storage cabinet and the liquid cooling unit to 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.

[0005] Specifically, 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 amount of charge or discharge 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 steps for obtaining the charge-discharge-temperature rise mapping space under the electrothermal conversion efficiency include: 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, using the electrothermal conversion efficiency, the current discharge voltage, 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 and the natural heat dissipation in the corresponding unit time and the effective temperature rise electrothermal conversion amount of the energy storage cabinet; S105. Obtain 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.

[0006] Specifically, the step of acquiring the charge-discharge-temperature rise mapping space under the electrothermal conversion efficiency further 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.

[0007] Specifically, 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 energy-saving and cooling control model includes the cooling optimization sub-model and the cooling control sub-model; The step of generating the first collaborative control parameter includes: 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.

[0008] Specifically, the cooling optimization sub-model includes a first cooling optimization sub-path and a second optimization sub-path; and the step of generating the first collaborative control parameter includes: 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.

[0009] Specifically, the step of obtaining the first target boost rate adjustment value includes: 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; According to the boost rate corresponding to the current abnormal voltage and the boost rate corresponding to the normal temperature rise of the battery pack in the energy storage cabinet, the abnormal change rate of the boost rate is obtained; The inverse of the abnormal change rate of the boost rate is taken to obtain a first target boost rate adjustment value.

[0010] Specifically, the step of obtaining the first target cooling optimization parameter set includes: 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 cooling map, obtaining a cooling 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.

[0011] Specifically, the step of obtaining the first target cooling optimization parameter set also includes: Based on the third cooling mapping, the cooling energy consumption of the liquid cooling unit is minimized and the cooling rate is maximized, and a cooling configuration optimization function in the first cooling optimization subpath is constructed; 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.

[0012] Specifically, the cooling control sub-model 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; 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.

[0013] The intelligent energy-saving control system of the liquid-cooled thermal management system of the energy storage cabinet includes: a data acquisition module, a mapping module, an instruction generation module and a control module; Data acquisition module, used to obtain energy storage cabinet working status data, thermal management system data, electric heat conversion model and energy-saving and cooling control model; A 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 and the electric-thermal conversion model; An instruction generation module, 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 a first coordinated control parameter and a 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 collaborative control device of the energy storage cabinet and the liquid cooling unit, control the time point of the change of the coolant circulation state in the liquid cooling unit and the cooling output power and adjust the boost rate of the energy storage cabinet.

[0014] Compared with the prior art, the present invention has the following beneficial effects: In view of the deficiencies in the prior art, the present invention acquires the working state data of the energy storage cabinet and the thermal management system data, and constructs the charge-discharge-temperature rise mapping space in combination with the electric-heat conversion model, so as to realize the in-depth analysis of the working state of the energy storage cabinet, and can accurately calculate the value of the abnormal temperature rise change rate of the electric power, and accordingly use the cooling optimization submodel in the energy-saving and cooling control model to generate the first target boost rate adjustment value and the first target cooling optimization parameter set, and then the cooling control submodel generates the liquid cooling control instruction and the boost adjustment instruction accordingly. Compared with the prior art, the cooling output efficiency and the boost rate can be dynamically adjusted according to the charge-discharge rate, so as to realize the two-way parallel adjustment of the energy-saving cooling and boosting process, and can quickly adjust the temperature anomaly, effectively reduce the cooling energy consumption, and greatly improve the stability, safety and energy utilization efficiency of the operation of the energy storage cabinet. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 This is a flow chart of an intelligent energy-saving control method for a liquid-cooled thermal management system of an energy storage cabinet according to Embodiment 1 of the present invention; Figure 2 This is a module diagram of the intelligent energy-saving control system of the liquid-cooled thermal management system of the energy storage cabinet according to Example 2 of the present invention. DETAILED DESCRIPTION

[0016] Example 1 The existing energy storage cabinet cooling technology generally only adjusts the corresponding cooling process, but does not adjust the abnormal charging and discharging boosting process. This results in the boosting abnormality not being resolved, and a large amount of cooling energy is required to adjust the heat released by the previous abnormal charging and the heat released by the abnormal boosting in the current state, which greatly increases the energy consumption of cooling. For this reason, 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 comprising: S1. Obtaining energy storage cabinet working status data, thermal management system data, electric heat conversion model and energy-saving and cooling control model; Furthermore, the energy storage cabinet working status data in this embodiment 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 charging or discharging voltage requirement; 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, the energy consumption data of the liquid cooling unit, and the location and heat dissipation distribution of the energy storage cabinet battery pack.

[0017] S2. According to the working state data of the energy storage cabinet and the electrothermal conversion model, 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 are obtained; Furthermore, the electric-heat conversion efficiency of this embodiment includes the charging heat conversion efficiency and the discharging heat conversion efficiency; The steps for obtaining the charge-discharge-temperature rise mapping space under the electrothermal conversion efficiency include: 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, using the electrothermal conversion efficiency, the current discharge voltage, 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 and the natural heat dissipation in the corresponding unit time and the effective temperature rise electrothermal conversion amount of the energy storage cabinet; Furthermore, the first electrothermal conversion energy consumption of this embodiment 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.

[0018] S105, obtaining a temperature rise change rate corresponding to the current discharge voltage according to the effective temperature rise electric heat conversion amount of the energy storage cabinet; 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.

[0019] Furthermore, the discharge-temperature rise mapping and charge-temperature rise mapping in this embodiment are obtained by fitting the current discharge voltage, the corresponding electrothermal conversion efficiency and the temperature rise change rate using the kernel function of the support vector machine.

[0020] 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.

[0021] This process lays a solid foundation for in-depth analysis of the operating status of the energy storage cabinet by comprehensively collecting the working status data of the energy storage cabinet and the data of the thermal management system. When obtaining the electric-thermal conversion efficiency and charge-discharge-temperature rise mapping space corresponding to the current working status of the energy storage cabinet, the electric-thermal conversion efficiency, energy consumption heat and temperature rise change rate are obtained through layer-by-layer analysis and calculation of data such as discharge voltage and charge, and the energy conversion and temperature change during the discharge process of the energy storage cabinet are accurately quantified. The mapping space is expanded to different voltage conditions to more comprehensively reflect the electric-thermal characteristics of the energy storage cabinet under various charging and discharging conditions, which enables the system to accurately grasp the heating trend of the energy storage cabinet under different working conditions, and provides an accurate basis for the subsequent dynamic adjustment of the cooling strategy and the boost rate according to the charging and discharging rate. Based on this, when facing different charging and discharging scenarios, more efficient temperature control can be achieved to avoid performance degradation and safety hazards caused by abnormal temperature, while helping to optimize energy utilization, reduce unnecessary energy consumption, and improve the overall operating efficiency and stability of the energy storage cabinet.

[0022] S3, 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 cooling control model to generate a first coordinated control parameter and a first coordinated control instruction; Furthermore, the energy-saving and cooling control model in the present embodiment includes a cooling optimization sub-model and a cooling control sub-model; further, the cooling optimization sub-model is constructed by a BP neural network optimized by a genetic algorithm, and two optimization paths are built into the 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, so that the overall sub-model can achieve a synchronous two-way optimization process.

[0023] Furthermore, the step of generating the first coordinated control parameter in this embodiment includes: 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.

[0024] Furthermore, during the calculation process, by adjusting the corresponding abnormal voltage boost rate while ensuring the minimum lower limit of charge and discharge, the charge and discharge requirements can be met while minimizing the abnormal voltage boost rate, thereby ensuring the safety of the charging equipment.

[0025] Furthermore, the cooling optimization sub-model in this embodiment includes a first cooling optimization sub-path and a second optimization sub-path; the step of generating the first collaborative control parameter includes: 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; Furthermore, in this embodiment, the step of obtaining the first target boost rate adjustment value includes: 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; According to the boost rate corresponding to the current abnormal voltage and the boost rate corresponding to the normal temperature rise of the battery pack in the energy storage cabinet, the abnormal change rate of the boost rate is obtained; The inverse of the abnormal change rate of the boost rate is taken to obtain a first target boost rate adjustment value.

[0026] 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.

[0027] This process calculates the predicted charging and discharging time length of the target object according to the target object's demand for charging or discharging and the corresponding charging and discharging voltage at the current moment, which provides a time dimension basis for the subsequent analysis of the thermal behavior of the battery during the entire charging and discharging process. Combined with the normal operating temperature range of the battery pack, the voltage rise rate corresponding to the normal temperature rise of the battery pack is obtained. 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 is calculated, and then the voltage rise rate corresponding to the current abnormal voltage is determined according to the mapping relationship of the charging-discharging-temperature rise mapping space. It is compared with the normal voltage rise rate to obtain the abnormal change rate of the voltage rise rate, and the opposite number is taken as the first target voltage rise rate adjustment value. This adjustment method takes the normal working state of the battery as the benchmark, and makes a reverse adjustment for the abnormal temperature rise situation, so that the voltage rise rate returns to a safe and reasonable range, thereby reducing the damage of the abnormal voltage rise to the equipment; through the above process, the voltage rise rate can be accurately calculated and adjusted, while meeting the charging and discharging requirements, the abnormal voltage rise rate is minimized, which is crucial to ensure the safety of the charging equipment. Abnormal voltage boost may cause the chemical reaction inside the battery to get out of control, accelerate battery aging, and even cause safety accidents such as overheating and fire. By properly adjusting the voltage boost rate, the battery life can be effectively extended to ensure the long-term stable operation of the energy storage system.

[0028] Furthermore, the step of obtaining the first target cooling optimization parameter set in this embodiment includes: 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; Furthermore, in this embodiment, by establishing the first cooling mapping, the corresponding relationship between the position point where the battery pack needs to be cooled and the corresponding cooling demand and the corresponding cooling pipeline can be clearly described, so that the subsequent cooling operation can accurately locate the area of ​​the battery pack where the actual heating is abnormal, avoiding the energy waste and low cooling efficiency caused by blind cooling; for example, when a corner of the energy storage cabinet heats up quickly due to the battery pack layout or local charging and discharging abnormalities, the first cooling mapping can clearly point out the position and match the corresponding cooling pipeline to ensure that the coolant can preferentially and effectively cool the area; further, in the actual operation process, the battery pack of the energy storage cabinet does not heat evenly, and the batteries at different positions have different heating conditions due to factors such as the charging and discharging state and heat conduction with other components; the configuration path of the circulation pipeline determines the position that the coolant can reach, and combined with the temperature change rate per unit time, the urgency of the cooling demand at each position can be determined. For example, if the temperature rise rate in a certain area is high, it means that the area needs stronger cooling. In this way, the first cooling map will establish a corresponding relationship between the area and the pipeline that can provide greater cooling capacity. In this way, the first cooling map constructed based on the corresponding relationship between spatial position and heat demand provides a basic framework for subsequent precise cooling.

[0029] 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; Furthermore, in this embodiment, based on the cooling target value corresponding to each circulation path obtained by the first cooling mapping, the second cooling mapping combines the output power of the liquid cooling unit, the circulation rate of the coolant in each circulation pipeline, the coolant temperature and flow rate and other parameters, and further refines the cooling capacity that each circulation path can actually achieve. This helps to reasonably plan the cooling task of each circulation path according to the actual cooling resource configuration, and avoid poor overall cooling effect due to excessive expectations for a certain circulation 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 actual use, the output power of the liquid cooling unit determines its total cooling 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 taken 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 map with these actual cooling parameters, the cooling rate that can be achieved in each circulation path under the existing conditions can be calculated, thereby constructing the second cooling map to achieve the matching of theoretical cooling demand and actual cooling capacity.

[0030] Based on the second cooling map, obtaining a cooling 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.

[0031] Based on the third cooling mapping, the cooling energy consumption of the liquid cooling unit is minimized and the cooling rate is maximized, and a cooling configuration optimization function in the first cooling optimization subpath is constructed; Furthermore, in the present 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 the subsequent optimization of the cooling strategy, and can minimize energy consumption while 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 to avoid energy waste caused by excessive cooling. For example, in some cases, increasing the cooling rate may significantly increase energy consumption, but the improvement in the overall cooling effect is not obvious. The third cooling mapping can help identify this situation, thereby optimizing the cooling plan.

[0032] 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.

[0033] Furthermore, in this embodiment, the constraint conditions are set by the above parameters because the circulation rate of the coolant is limited by the characteristics of the pipeline, the performance of the pump and the safety of the system. If the circulation rate is too low, the expected cooling effect may not be achieved; if it is too high, the pipeline pressure may be too high, increasing the wear and failure risk of the system. Therefore, the upper and lower limits of the circulation rate are set to ensure system safety.

[0034] According to the physical properties of the coolant, such as the specific heat capacity and boiling point of ethylene glycol aqueous solution, there is a reasonable maximum acceptable temperature reduction range. Beyond this range, the coolant may lose its cooling ability or even damage the pipeline or other equipment. Therefore, the cooling temperature of the coolant is limited by this limit value, and a reasonable coolant circulation rate and flow rate are designed according to the cooling temperature, so that the temperature of the coolant used for cooling is maintained at the optimal cooling temperature, that is, the fastest and most heat is absorbed, and the coolant that has absorbed the heat can be quickly replaced through the designed reasonable circulation rate.

[0035] The output power of the liquid-cooled unit is limited by the unit's own design and electrical and mechanical properties. 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 solved by the constraint conditions and the cooling configuration optimization function can greatly reduce energy consumption.

[0036] Further, 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; When it is detected at the corresponding time point that the temperature change rate and the voltage boost rate are greater than the corresponding normal temperature 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; further, in this embodiment, the cooling control sub-model is constructed by a pre-trained reinforcement learning algorithm.

[0037] The energy-saving and cooling control model of this process generates collaborative control parameters and instructions based on the charge-discharge-temperature rise mapping space and the thermal management system data to achieve precise control of the energy storage cabinet. By calculating the target object's predicted charge and discharge time length, combined with the normal operating temperature range of the battery pack, the normal boost rate and normal temperature rise change rate can be accurately obtained, and compared with the actual temperature rise change rate to obtain the abnormal temperature rise change rate value, providing the core basis for subsequent targeted adjustments; for example, when it is detected that the boost rate and temperature rise change rate are beyond the normal range, the system can respond quickly and accurately calculate the first target boost rate adjustment value through the second optimization subpath. In this process, based on the abnormal heat energy release and the charge-discharge-temperature rise mapping relationship, the boost rate corresponding to the abnormal voltage is determined, and then the abnormal change rate of the boost rate is obtained. Finally, the opposite number is taken as the adjustment value, which can quickly adjust the boost 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, fully considering factors such as the circulation pipeline configuration, battery pack distribution, and liquid cooling unit operating parameters, the refined management of cooling energy consumption is achieved. From the first cooling mapping to the third cooling mapping, the cooling demand, cooling rate and energy consumption relationship of each circulation path are gradually analyzed in depth. On this basis, a cooling configuration optimization function is constructed, with the goal of minimizing the cooling energy consumption and maximizing the cooling rate of the liquid cooling unit, and combining the maximum 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 energy consumption and improve energy utilization efficiency while meeting the cooling demand. In addition, the cooling output power and the energy storage cabinet boost rate are synchronously adjusted to effectively protect the energy storage cabinet and related equipment. When an abnormal situation is detected, the first control sub-path generates the first liquid cooling control instruction according to the first target cooling optimization parameter set, and adjusts the working state of the liquid cooling unit in time to ensure that the temperature of the energy storage cabinet is maintained in the normal operating temperature range, preventing battery performance degradation, shortened life, and even safety accidents caused by overheating. The second control subpath generates a second boost regulation instruction through the first target boost rate regulation value, reasonably controls the boost rate, avoids damage to the energy storage cabinet due to abnormal voltage changes, and reduces heat output while meeting the charging and discharging requirements. This coordinated regulation mechanism protects the energy storage cabinet equipment in all directions and improves the reliability and stability of the system.

[0038] S4. Feedback the first collaborative control parameter and the first collaborative control instruction to the collaborative control device for the energy storage cabinet and the liquid cooling unit, control the time point of the coolant circulation state change and the cooling output power in the liquid cooling unit, and adjust the boost rate of the energy storage cabinet, so that the energy storage cabinet and the thermal management system can operate at the lowest energy consumption state while meeting the charging and discharging requirements.

[0039] Furthermore, in this embodiment, the first liquid cooling control instruction is input into the control device corresponding to the liquid cooling unit to control the output power of the corresponding liquid cooling unit and the coolant flow and flow rate of the corresponding circulation path, so that the cooling rate on the corresponding circulation path is greater than the temperature rise rate of the battery pack caused by charging and discharging in real time. At the same time, the second boost adjustment instruction is input into the charge and discharge control device to adjust the abnormal boost rate of charge and discharge.

[0040] Example 2 See also 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, comprising: a data acquisition module, a mapping module, an instruction generation module and a control module; Data acquisition module, used to obtain energy storage cabinet working status data, thermal management system data, electric heat conversion model and energy-saving and cooling control model; A 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 and the electric-thermal conversion model; An instruction generation module, 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 a first coordinated control parameter and a 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 collaborative control device of the energy storage cabinet and the liquid cooling unit, control the time point of the change of the coolant circulation state in the liquid cooling unit and the cooling output power and adjust the boost rate of the energy storage cabinet.

[0041] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation modes, which are merely illustrative rather than restrictive. Under the guidance of the present invention, ordinary technicians in the field may also change, modify, replace and modify the above-mentioned embodiments without departing from the scope of protection of the purpose of the present invention and the claims, and all of these are within the protection 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, electric heat conversion model and energy-saving and cooling control model; According to the working state data of the energy storage cabinet and the electrothermal conversion model, 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 are obtained; Inputting the charge-discharge-temperature rise mapping space under the electric-heat 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; The first collaborative control parameter and the first collaborative control instruction are fed back to the collaborative control device of the energy storage cabinet and the liquid cooling unit to 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.

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 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, using the electrothermal conversion efficiency, the current discharge voltage, 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 and the natural heat dissipation in the corresponding unit time and the effective temperature rise electrothermal conversion amount of the energy storage cabinet; S105. Obtain 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.

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 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.

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 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.

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 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 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.

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 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.

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 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.

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 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.

9. The intelligent energy-saving control method for the liquid-cooled thermal management system of the energy storage cabinet according to claim 8, characterized in that: The cooling control sub-model 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 subpath corresponds to the second optimization subpath; The first control subpath generates a first liquid cooling control instruction according to a 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.

10. 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 9, 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, the electric heat conversion model and the energy-saving and temperature-reducing 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 and the electric-thermal conversion model; 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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