Dynamic thermal management method and system for cascade thermal coupling control system

Abstract

The invention belongs to the technical field of thermal management, and particularly relates to a dynamic thermal management method and system for a cascade thermal coupling control system. The method comprises the following steps: judging the running state of the current cascade thermal coupling control system; based on the operation state, obtaining operation parameters of the cascade thermal coupling control system in real time; adjusting the heat flow path according to the operation parameters to obtain an actual heat flow value; obtaining a heat flow prediction value by using the heat flow prediction model, and comparing the heat flow prediction value with an actual heat flow value to obtain a heat flow difference value; and adjusting operation parameters of the cascade thermal coupling control system according to the heat flow difference value, and controlling the dynamic heat flow of the cascade thermal coupling control system. According to the invention, the objective function is constructed based on the LSTM neural network, and the state of the phase change material can be adjusted in real time in cooperation with the phase change progress-resistance value mapping model, so that the distribution of the heat flow is realized, and the dynamic management of the cascade thermal coupling control system is completed.

Description

Technical Field

[0001] This disclosure belongs to the field of thermal management technology, and specifically relates to a dynamic thermal management method and system for a cascade thermal coupling control system. Background Technology

[0002] In existing technologies, the low-grade waste heat of 40-50℃ generated during the operation of flow batteries is not effectively utilized. In traditional compressed-air energy storage (CAES) systems, the high-temperature heat of 150-300℃ generated during the compression stage is lost in 60-70% of the heat in the cascaded thermal coupling control system, and the independent operation of the two leads to low overall system efficiency.

[0003] Flow batteries and CAES typically operate independently, without achieving synergistic utilization of heat. For example, inventions published in CN109737036A, CN116335781A, and CN117759509A propose the integration of compressed air energy storage and waste heat recovery, but do not address the cascade utilization of waste heat from flow batteries.

[0004] To address the aforementioned technical problems, this disclosure provides a dynamic thermal management method and system for a cascade thermal coupling control system. Summary of the Invention

[0005] To address the above problems, this disclosure provides a dynamic thermal management method for a cascade thermal coupling control system, the method comprising: Determine the current operating status of the cascade thermal coupling control system; Based on the operating status, the operating parameters of the cascade thermal coupling control system are acquired in real time; Adjust the heat flow path according to the operating parameters to obtain the actual heat flow value; The predicted heat flow value is obtained by using a heat flow prediction model and compared with the actual heat flow value to obtain the heat flow difference. The operating parameters of the cascade thermal coupling control system are adjusted based on the heat flow difference to control the dynamic heat flow of the cascade thermal coupling control system.

[0006] According to some embodiments of this disclosure, the operating state includes a charging state and a discharging state.

[0007] According to some embodiments of this disclosure, when the operating state is charging state, the operating parameters of the cascade thermal coupling control system are acquired in real time, including at least: flow battery temperature, CAES intake pipe temperature, first-stage compressor outlet temperature and phase change material resistivity. When the operating state is in discharge state, the operating parameters of the cascade thermal coupling control system are acquired in real time, including at least: phase change material resistivity, gas storage tank pressure and flow battery temperature.

[0008] According to some embodiments of this disclosure, adjusting the heat flow path based on operating parameters to obtain the actual heat flow value includes: Construct the objective function, input the running parameters, and obtain the heat flow command; Adjust the heat flow path according to the heat flow command to obtain the actual heat flow value.

[0009] According to some embodiments of this disclosure, an objective function is constructed, and operating parameters are input to obtain a heat flow command, including: Constructing the objective function based on LSTM neural network; By inputting the operating parameters into the objective function, the heat required for each path of the cascade thermal coupling control system is obtained; Generate heat flow instructions for each path based on the heat required for each path.

[0010] According to some embodiments of this disclosure, adjusting the operating parameters of the cascade thermal coupling control system based on the heat flow difference, and controlling the dynamic heat flow of the cascade thermal coupling control system includes: The phase change progress-resistance value mapping model parameters are updated based on the heat flow difference to obtain a new phase change progress-resistance value mapping model; The operating parameters of the cascade thermal coupling control system are adjusted by using a new phase change progress-resistance value mapping model. The heat flow distribution of the cascade thermal coupling control system is dynamically managed based on the adjusted operating parameters.

[0011] This disclosure also provides a dynamic thermal management system for a cascade thermal coupling control system, the system comprising: The judgment module is used to determine the current operating status of the cascade thermal coupling control system; The operating parameter module is used to acquire the operating parameters of the cascade thermal coupling control system in real time based on the operating status. The actual heat flux value module is used to adjust the heat flux path according to the operating parameters to obtain the actual heat flux value; The heat flux difference module is used to obtain the predicted heat flux value using the heat flux prediction model and compare it with the actual heat flux value to obtain the heat flux difference. The adjustment module is used to adjust the operating parameters of the cascade thermal coupling control system according to the heat flow difference, thereby controlling the dynamic heat flow of the cascade thermal coupling control system.

[0012] According to some embodiments of this disclosure, the actual heat flux value module is used to adjust the heat flux path according to operating parameters to obtain the actual heat flux value, including: The actual heat flow value module is used to construct the objective function, input the operating parameters, and obtain the heat flow command; Adjust the heat flow path according to the heat flow command to obtain the actual heat flow value.

[0013] According to some embodiments of this disclosure, an objective function is constructed, and operating parameters are input to obtain a heat flow command, including: Constructing the objective function based on LSTM neural network; By inputting the operating parameters into the objective function, the heat required for each path of the cascade thermal coupling control system is obtained; Generate heat flow instructions for each path based on the heat required for each path.

[0014] According to some embodiments of this disclosure, an adjustment module is used to adjust the operating parameters of the cascade thermal coupling control system based on the heat flow difference, thereby controlling the dynamic heat flow of the cascade thermal coupling control system, including: The adjustment module is used to update the phase change progress-resistance value mapping model parameters based on the heat flow difference, and obtain a new phase change progress-resistance value mapping model. The operating parameters of the cascade thermal coupling control system are adjusted by using a new phase change progress-resistance value mapping model. The heat flow distribution of the cascade thermal coupling control system is dynamically managed based on the adjusted operating parameters.

[0015] This disclosure has the following beneficial effects: (1) This disclosure utilizes a three-stage heat exchange network to realize the cascade utilization of high-temperature waste heat from flow batteries and CAES; (2) This disclosure constructs an objective function based on an LSTM neural network, and in conjunction with a phase change progress-resistance value mapping model, it can adjust the state of the phase change material in real time, thereby realizing the distribution of heat flow and completing the dynamic management of the cascade thermal coupling control system. (3) In this disclosure, the phase change thermal energy storage material uses modification technology to improve the storage efficiency of CAES compression heat.

[0016] Other features and advantages of this disclosure will be set forth in the following description and will be apparent in part from the description or may be learned by practicing the disclosure. The objects and other advantages of this disclosure may be realized and obtained by means of the structures pointed out in the description and the accompanying drawings. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of this disclosure or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this disclosure. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 A diagram of a cascade thermal coupling control system in an embodiment of this disclosure is shown; Figure 2A diagram illustrating a dynamic thermal management method for a cascade thermal coupling control system in an embodiment of this disclosure is shown. Figure 3 A detailed flowchart of the dynamic thermal management method for a cascade thermal coupling control system is shown in an embodiment of this disclosure; Figure 4 This diagram illustrates the dynamic thermal management algorithm flowchart in an embodiment of this disclosure. Figure 5 A diagram of a dynamic thermal management system for a cascade thermal coupling control system is shown in an embodiment of this disclosure. Detailed Implementation

[0019] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided to make this disclosure more comprehensive and complete, and to fully convey the concept of the example embodiments to those skilled in the art. The described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a full understanding of embodiments of this disclosure. However, those skilled in the art will recognize that the technical solutions of this disclosure can be practiced with one or more of the specific details omitted, or other methods, components, apparatus, steps, etc., can be employed. In other instances, well-known technical solutions are not shown or described in detail to avoid obscuring various aspects of this disclosure.

[0020] Furthermore, the accompanying drawings are merely illustrative of this disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities may be implemented in software, in one or more hardware units or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0021] The flowchart shown in the attached diagram is merely an illustrative example and does not necessarily include all steps. For example, some steps may be broken down, while others may be combined or partially combined; therefore, the actual execution order may change depending on the specific circumstances.

[0022] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented, for example, in orders other than those illustrated or described herein.

[0023] Furthermore, the terms “comprising” and “having”, and any variations thereof, are intended to cover non-exclusive inclusion, such that a process, method, system, product, or device that includes a series of steps or sub-modules is not necessarily limited to those steps or sub-modules that are explicitly listed, but may include other steps or sub-modules that are not explicitly listed or that are inherent to such process, method, product, or device.

[0024] like Figure 1 As shown, the cascade thermal coupling control system in this application includes a flow battery module, a three-stage heat exchange network, a phase change thermal storage module, and a compressed air energy storage unit. 1. Flow battery module: Vanadium redox flow battery pack (power 1-10MW, electrolyte circulation temperature 40-55℃), waste heat is output through cooling circuit; 2. Three-level hot-swapping network: First-stage heat exchanger: Connects the flow battery cooling circuit to the CAES cascade thermal coupling control system intake pipe, transferring the flow battery waste heat (40-50℃) to the CAES cascade thermal coupling control system intake, preheating it to 80℃, reducing compression power consumption by 12-15%. Secondary heat exchanger: Integrates phase change thermal storage material with the compressor interstage cooling system, storing the high-temperature heat of 150℃ generated by CAES compression. The phase change thermal storage material can be a hydrated salt phase change material (such as magnesium chloride hexahydrate). ), composite phase change materials (such as hydrated salt-expanded graphite composites, (+expanded graphite), high-temperature thermal storage materials (such as molten salt, This improves the storage efficiency of compression heat in the CAES cascade thermal coupling control system. Three-stage heat exchanger: connects the expander exhaust port to the electrolyte temperature control box of the flow battery, and uses the CAES cascade thermal coupling control system to maintain the electrolyte temperature stability (±2℃) by utilizing the waste heat of the expansion exhaust (80℃). 3. Phase change thermal energy storage module: Charging phase (off-peak electricity hours, heat storage process): The high-temperature heat generated during the compression phase of the compressed air energy storage (CAES) system is recovered and stored in the phase change material to avoid heat waste; Discharge phase (peak power period, heat release process): release the heat stored in the phase change thermal storage module, heat the air intake of the expander, and improve the power generation efficiency of the CAES system; 4. Compressed air energy storage unit: Two-stage compression + regenerative expansion structure, suitable for salt cavern gas storage (pressure cascade thermal coupling control system 6-10MPa).

[0025] The working principle of the cascade thermal coupling control system is as follows: 1. Charging stage (off-peak electricity period): Waste heat from the flow battery is preheated by the primary heat exchanger and then introduced into the CAES cascade thermal coupling control system. The high-temperature heat generated by compression is stored in the phase change thermal storage module.

[0026] Operation process: 1. Heat generation: The compressor performs two-stage compression of air. During the compression process, the air temperature rises to 150~200℃ (depending on the compression ratio) in the staged thermal coupling control system, generating a large amount of compression heat.

[0027] 2. Heat is transferred to the phase change thermal storage module: The compressed high-temperature air exchanges heat with the phase change thermal storage module through a secondary heat exchanger.

[0028] Phase change materials melt endothermally: For example (phase change temperature 117℃, latent heat 180 kJ / kg), after absorbing the heat of compression, the phase change material gradually melts from solid to liquid, storing the heat in the form of latent heat.

[0029] Heat exchange efficiency optimization: Titanium-vanadium alloy plate heat exchanger (K ​​value ≥ 3000) This ensures rapid heat transfer at high temperatures and reduces heat loss. A dynamic thermal management algorithm adjusts the heat flow distribution in the secondary heat exchanger, ensuring uniform heat absorption by the phase change material and preventing localized overheating.

[0030] 3. Heat storage complete: After the phase change material is completely melted, it enters a constant temperature heat storage state, waiting for the discharge stage to release heat.

[0031] II. Discharge stage (peak power period): The phase change thermal storage module releases heat to heat the expander intake air to 200°C and the exhaust heat of the expansion is used to maintain the electrolyte temperature of the flow battery through a three-stage heat exchanger.

[0032] Operation process 1. Triggered by heat release demand: When the power grid enters peak power period, the system switches to discharge mode, and the expander needs high-temperature air to drive power generation.

[0033] 2. Exothermic solidification of phase change materials: The phase change thermal energy storage module is connected to the expander inlet pipeline through a three-stage heat exchanger; Phase change material exothermic solidification: The liquid phase change material releases the stored latent heat and gradually solidifies into a solid when the temperature drops below 117°C in the stepped thermal coupling control system. The heat is transferred to the expansion machine intake. Temperature enhancement effect: The expander inlet temperature is increased from room temperature (25℃) to over 200℃, improving the power efficiency by 15-20%. During the heat release process, the phase change material maintains constant temperature characteristics, ensuring stable expander inlet temperature and avoiding the impact of temperature fluctuations on power generation efficiency.

[0034] 3. Secondary utilization of waste heat: The exhaust temperature of the expander is about 80℃, which is transferred to the electrolyte temperature control system of the flow battery through a three-stage heat exchanger. This maintains the electrolyte temperature in the ideal range of 40-55℃ of the cascade thermal coupling control system, thereby improving the battery cycle life.

[0035] Table 1 shows a comparison of the charging and discharging stages of the cascade thermal coupling control system.

[0036] Table 1

[0037] Compared with traditional systems, the cascade thermal coupling control system in this application has the following advantages, as shown in Table 2: Table 2

[0038] like Figure 2 As shown, this disclosure provides a dynamic thermal management method for a cascade thermal coupling control system, the method comprising: Determine the current operating status of the cascade thermal coupling control system; Based on the operating status, the operating parameters of the cascade thermal coupling control system are acquired in real time; Adjust the heat flow path according to the operating parameters to obtain the actual heat flow value; The predicted heat flow value is obtained by using a heat flow prediction model and compared with the actual heat flow value to obtain the heat flow difference. The operating parameters of the cascade thermal coupling control system are adjusted based on the heat flow difference to control the dynamic heat flow of the cascade thermal coupling control system.

[0039] Specifically, the operating states include charging state and discharging state.

[0040] Specifically, when the operating state is charging, the operating parameters of the cascade thermal coupling control system are acquired in real time, including at least: the flow battery temperature, the CAES intake pipe temperature, the first-stage compressor outlet temperature, and the phase change material resistivity. When the operating state is in discharge state, the operating parameters of the cascade thermal coupling control system are acquired in real time, including at least: phase change material resistivity, gas storage tank pressure and flow battery temperature.

[0041] Specifically, the heat flow path is adjusted according to the operating parameters to obtain the actual heat flow value, including: Construct the objective function, input the running parameters, and obtain the heat flow command; Adjust the heat flow path according to the heat flow command to obtain the actual heat flow value.

[0042] Specifically, an objective function is constructed, and operating parameters are input to obtain heat flow instructions, including: Constructing the objective function based on LSTM neural network; By inputting the operating parameters into the objective function, the heat required for each path of the cascade thermal coupling control system is obtained; Generate heat flow instructions for each path based on the heat required for each path.

[0043] Specifically, adjusting the operating parameters of the cascade thermal coupling control system based on the heat flow difference, and controlling the dynamic heat flow of the cascade thermal coupling control system includes: The phase change progress-resistance value mapping model parameters are updated based on the heat flow difference to obtain a new phase change progress-resistance value mapping model; The operating parameters of the cascade thermal coupling control system are adjusted by using a new phase change progress-resistance value mapping model. The heat flow distribution of the cascade thermal coupling control system is dynamically managed based on the adjusted operating parameters.

[0044] like Figure 3 As shown, the detailed technical solution of this method is as follows: Data acquisition layer: Real-time acquisition of system operating parameters, including: flow battery temperature, charging and discharging power, CAES inlet pressure, compressor temperature, phase change material state (temperature, phase change progress), etc. Predictive model layer: Predicts heat flow demand based on historical data and generates the optimal heat flow command Q_opt; Control execution layer: Adjusts the heat flow path according to the cascade thermal coupling control system Q_opt, including: three-way valve opening (controlling the heat flow ratio), pump power (adjusting fluid flow rate), etc. Feedback correction layer: By comparing the actual heat flow with the predicted value, the parameters of the phase change progress-resistance value mapping model are corrected to improve the prediction accuracy.

[0045] Charging phase (heat storage + preheating): Data acquisition: flow battery cooling circuit temperature, CAES cascade thermal coupling control system inlet air temperature, compressor outlet temperature; Model Calculation: The LSTM cascade thermal coupling control system model calculates the required heat storage capacity (e.g., target heat storage power of 5MW) based on grid off-peak load forecasts. Command Generation: For example, the first-stage heat exchanger needs to transfer 3MW of heat to preheat air to 80°C, and the second-stage heat exchanger needs to transfer 2MW of heat to the phase change thermal storage module.

[0046] Execution regulation: A three-way valve regulates the heat flow distribution ratio, ensuring that waste heat from the flow battery is preferentially used to preheat air, with remaining heat stored. A variable frequency pump increases the flow velocity on the phase change material side to enhance heat transfer efficiency.

[0047] Discharge stage (heat release + waste heat recovery): Data acquisition: phase change material temperature, expander inlet air requirement temperature, flow battery electrolyte temperature, etc.

[0048] Model Calculation: The LSTM cascade thermal coupling control system model calculates the heat required by the expander based on the peak power load of the power grid (e.g., a target heat release power of 4MW). Command Generation: For example, the secondary heat exchanger releases 3.5MW of heat to heat the expander intake air to 200℃, and the tertiary heat exchanger recovers 0.5MW of waste heat to maintain the electrolyte temperature.

[0049] Execution adjustment: The three-way valve switches the heat flow path, prioritizing the expander's needs, while remaining waste heat is directed to the liquid flow battery temperature control box. The electric valve regulates the heat release rate of the phase change material, ensuring that the expander's inlet temperature fluctuation is <±3℃.

[0050] This method is deeply related to heat exchange: (1) Multi-objective optimization algorithm Objective function:

[0051] in: This indicates the expander inlet air temperature deviation (target 200℃). This indicates the electrolyte temperature deviation in a flow battery (target 50°C). This indicates the heat loss rate of the phase change thermal storage module; All represent weighting coefficients, which are dynamically adjusted, such as during peak electricity hours. priority.

[0052] Heat exchange correlation: By adjusting the heat flow distribution of each stage of heat exchangers, the power generation efficiency of CAES and the temperature control requirements of flow batteries are balanced, avoiding system imbalance caused by single-objective optimization.

[0053] (2) Real-time monitoring of phase transition state Technical Implementation: Based on electrical resistance tomography (ERT) technology, the solid-liquid interface distribution of phase change materials is reconstructed in real time. A phase change progress-resistance value mapping model is established. Where: s is the degree of phase transition (0=solid, 1=liquid). The initial resistivity, is a material constant.

[0054] Heat exchange correlation: When the solid content of the phase change material is greater than 60%, the algorithm automatically increases the heat flux density of the secondary heat exchanger to avoid heat transfer deterioration caused by local solidification (ensuring the cycle life of the ≥1000-stage thermal coupling control system in this application).

[0055] (3) Anti-interference compensation mechanism Interference source identification External disturbances: Sudden changes in ambient temperature (e.g., -20℃) 35℃), sudden change in power grid command.

[0056] Internal disturbances: Heat exchanger scaling (heat transfer coefficient decreases by 10%), phase change material aging (latent heat decays by 5%).

[0057] Compensation strategy: cascade thermal coupling control system Feedforward control: Adjust the thermal storage strategy in advance based on weather forecasts (e.g., increase the thermal storage cascade thermal coupling control system by 15% on low-temperature days).

[0058] Adaptive filtering: The degree of fouling in the heat exchanger is estimated in real time by extended Kalman filtering (EKF), and the parameters of the phase change progress-resistance value mapping model are dynamically corrected.

[0059] This method is compared with traditional techniques: Key performance indicators

[0060] Case Study on Improving Heat Exchange Efficiency Scene: A 50MW photovoltaic + energy storage power station in Haixi Prefecture, Qinghai Province, with an ambient temperature of -15℃.

[0061] Traditional control: Flow battery waste heat utilization rate is 40%, CAES cycle efficiency is 52%, and electrolyte temperature fluctuation is ±5℃.

[0062] Dynamic algorithm control: waste heat utilization rate is increased to 70%, CAES cycle efficiency reaches 60%, and electrolyte temperature is stabilized at 48±1℃ (verification of efficiency decay of <3% in the above-mentioned -20℃ environment).

[0063] The integrated architecture of algorithms and heat exchange systems, such as Figure 4 As shown, the dynamic thermal management algorithm achieves deep collaboration between the three-level heat exchange network and the phase change thermal storage module through the closed-loop mechanism of the cascade thermal coupling control system, which consists of "data-driven prediction cascade thermal coupling control system - model optimization control cascade thermal coupling control system - real-time feedback correction cascade thermal coupling control system". This solves the problems of response lag and multi-objective conflict in traditional thermal management systems and is the core technology for improving the efficiency and stability of the cascade thermal coupling system.

[0064] The objective function established in this method aims to quantify the multi-objective optimization requirements of the system, and to balance the three core contradictions in the cascaded thermal coupling system of flow battery and compressed air energy storage (CAES) through mathematical modeling: Temperature stability conflict: CAES expander requires stable high-temperature air intake (target 200℃) to maximize power generation efficiency, while flow battery electrolyte requires precise temperature control (target 50℃) to extend cycle life. The two may conflict in their heat flow distribution requirements (e.g., whether to prioritize power generation or battery temperature control during the heat release phase).

[0065] Energy loss contradiction: heat loss of phase change thermal storage module It is negatively correlated with heat flow distribution efficiency: excessive pursuit of temperature accuracy in a certain stage may lead to heat waste in other stages (such as insufficient heat intake of the expander due to overly strict temperature control of the flow battery).

[0066] Dynamic response contradictions: Power grid load fluctuations (such as peak-valley power switching) and changes in ambient temperature (such as day-night temperature difference) require the system to quickly adjust the heat flow distribution strategy. Traditional fixed parameter control is difficult to balance multiple objectives in a dynamic manner.

[0067] The core functionalities that can be achieved using the objective function are as follows: 1. Multi-objective optimization decision making Quantitative priority control Through weighting coefficients ( Dynamically adjust system optimization objectives, such as: peak power periods (power generation priority): increase (Expander temperature weighting) ensures maximum CAES power generation efficiency; Battery maintenance period: Increase (Electrolyte temperature weighting) to ensure flow battery life; system maintenance periods: increase (Heat loss weight) to reduce long-term operating energy costs.

[0068] Case Application When the power grid enters peak electricity consumption (high electricity prices), the algorithm automatically... The efficiency was increased from 0.4 to 0.6, prioritizing the expansioner intake temperature to be stable at 200±3℃. At this point, the flow battery temperature is allowed to fluctuate within the range of 45-55℃, in exchange for a 2-3% increase in power generation efficiency.

[0069] 2. Precise calculation of heat flux distribution Mathematical mapping relationship: The objective function transforms temperature deviation and heat loss into calculable numerical indicators, and determines the optimal heat flow distribution scheme by finding the minimum value: If If the (expander temperature deviation) is too large, the algorithm instructs the secondary heat exchanger to increase its heat release power, while allowing... (Battery temperature deviation) increases moderately within the constraints; if If heat loss exceeds a threshold (e.g., >5%), the algorithm adjusts the opening of the three-way valve to reduce unnecessary heat flow circulation.

[0070] Calculation Example When the system detects the heat loss rate of the phase change thermal storage module =8% (exceeding the standard). The objective function calculation shows that the heat flux ratio of the tertiary heat exchanger needs to be reduced from 30% to 20%, and the excess heat should be directed to the secondary heat exchanger. Reduce to below 5%, while ensuring <±5℃.

[0071] 3. Dynamic compensation and anti-interference Interference Quantification Assessment: The objective function can assess the impact of external disturbances on the system in real time, for example: When the ambient temperature drops by 10°C, Increase the output compensation command of the objective function: increase the heat release power of the phase change thermal storage module by 10%, and simultaneously activate electric heating to maintain the flow battery temperature; when fouling of the heat exchanger causes a 5% decrease in the heat transfer coefficient, The objective function is increased, triggering a cleaning warning and temporarily increasing the pump flow rate by 15% to compensate for heat transfer efficiency.

[0072] Adaptive adjustment mechanism: By iteratively optimizing the weight coefficients, the objective function can adapt to equipment aging (such as the latent heat decay of phase change materials) or changes in operating conditions (such as the decrease in air pressure due to increased altitude), ensuring that the long-term operating efficiency decay of the system is less than 3% (experimental verification data).

[0073] 4. System energy efficiency and economic optimization Energy efficiency improvement path: The objective function directly drives an 8-10% improvement in system efficiency by minimizing the overall error: avoiding the phenomenon of "overcooling" or "heat waste" in traditional control, for example: accurately allocating the waste heat of the flow battery during the charging stage (65-70% for preheating air, 30-35% for storage), reducing compression power consumption by 12-15% compared to the traditional fixed ratio (50%:50%). Optimizing the priority of waste heat recovery during the discharge phase increases the utilization rate of waste heat from expander exhaust from 50% to 95%, and further reduces the energy consumption of flow battery temperature control by 20%.

[0074] Economic Quantification: Optimizing the objective function can effectively increase annual revenue, save costs, and shorten the investment cost recovery period.

[0075] The objective function, as the "decision core" of the dynamic thermal management algorithm, transforms complex physical contradictions into a computable optimization problem through mathematical modeling. It enables precise control of heat flow distribution, quantitative compensation for system disturbances, and synergistic optimization of energy efficiency and economy, and is a key technical link supporting the efficient and stable operation of cascade thermal coupling systems.

[0076] The phase transition progress-resistance value mapping model established in this method aims to non-invasively monitor the solid-liquid state of phase change materials in real time, solving the technical problem that traditional temperature sensors cannot directly reflect the phase transition progress. Specifically, it includes: Precise perception of phase transition state Phase change materials (such as During the solid-liquid transition, resistivity changes systematically with the progress of the phase change. However, temperature sensors can only monitor local temperatures and cannot directly obtain the solid-liquid ratio of the material (e.g., 60% solid and 40% liquid). This model converts the abstract phase change progress (s) into a quantifiable electrical signal through resistance measurement, enabling "visualized" monitoring of the internal state of the thermal storage module.

[0077] Heat exchange efficiency optimization The solid-liquid distribution of phase change materials directly affects heat exchange efficiency: when the solid content is too high, heat transfer paths may be blocked (e.g., local solidification), leading to a 10-20% decrease in the heat transfer coefficient of the secondary heat exchanger; when the liquid content is too high, thermal stratification may occur, affecting the uniformity of heat release. The model provides real-time phase change state data for dynamic thermal management algorithms, supporting adjustments to heat flux distribution strategies (e.g., increasing heat flux density to prevent local solidification when the solid content is >60%).

[0078] System security warning When the phase change material undergoes an abnormal phase change due to overcooling or overheating (such as s > 1 and still not being completely liquid), the resistance value will deviate from the model prediction range, triggering a system warning to avoid the risk of thermal runaway due to phase change failure (such as local overheating of the thermal storage module to above 150°C).

[0079] 2. Physical meaning of the model output Through measured resistivity Substituting into the model, the phase transition progress s can be calculated, and thus the following can be obtained: Solid-liquid ratio: Solid percentage = (1-s)×100%, Liquid percentage = s×100%; Thermal storage status assessment: s < 0.3 indicates insufficient thermal storage, and s > 0.7 indicates sufficient thermal storage, which guides the heat flow distribution decision during the charging and discharging phases.

[0080] 3. Application of the model in relation to the heat exchange process Charging phase (thermal storage control) When the model calculates s=0.5 (50% solid state), the dynamic thermal management algorithm instructs the secondary heat exchanger to increase the heat flux density to 2000 W / m³. 2 This accelerates the melting of phase change materials and avoids a decrease in heat transfer efficiency due to solid residue.

[0081] Discharge stage (heat release optimization) If the model feedback s=0.2 (80% solid) but the heat release power is insufficient, the algorithm judges that there may be local solidification, and immediately adjusts the three-way valve to increase the heat flow rate to 3m / s. By forcing convection, the solidified layer is destroyed, ensuring that the heat release efficiency is ≥95%.

[0082] Cyclic life management During long-term operation, if the model finds that the resistivity increases for the same s (e.g.) From 100 Rise to 120 This indicates aging of the phase change material (such as loss of crystal water), triggers maintenance instructions (such as adding distilled water), and extends the cycle life to more than 1,000 cycles.

[0083] This model provides a low-cost and highly reliable means of monitoring the phase change state of a cascade thermal coupling system by quantitatively relating resistivity to the phase change progress. The calculated s-value directly characterizes the solid-liquid ratio of the phase change material and is a key data support for dynamically adjusting the heat exchange process and ensuring system efficiency and safety.

[0084] Example This embodiment is based on a 10MW vanadium redox flow battery and compressed air energy storage (CAES) cascade thermal coupling system, applied to a power grid peak shaving scenario. The system configuration is as follows:

[0085] I. Operation process during the charging phase (a) Data Acquisition and Initial State During off-peak electricity hours (0:00-6:00 AM), the system initiates the charging process. At this time, the initial states of each component are as follows: Vanadium redox flow battery: After discharge, the electrolyte temperature is 42℃, and charging begins with a charging power of 5MW, generating a waste heat flow of 3MW.

[0086] Compressed air energy storage system: The initial air pressure is 0.1MPa and the temperature is 25℃ before entering the first-stage compressor.

[0087] Phase change thermal energy storage module: in a completely solid state, at a temperature of 40℃, with a resistivity of 100. . The sensors collect data in real time and transmit it to the dynamic thermal management controller as follows: Temperature sensor for flow battery cooling circuit: 42℃; CAES intake manifold temperature sensor: 25℃; Primary compressor outlet temperature sensor: 120℃; Phase change thermal storage module resistivity sensor: 100 .

[0088] (II) Algorithm Decision Making and Heat Flow Allocation The dynamic thermal management algorithm predicts the heat flow demand for the next 15 minutes based on an LSTM model, with the objective function being... ;in, This indicates the expander inlet air temperature deviation (target 200℃). This indicates the electrolyte temperature deviation in a flow battery (target 50°C). This indicates the heat loss rate of the phase change thermal storage module; All represent weighting coefficients, which are dynamically adjusted, such as during peak electricity hours. priority; The calculation shows that: Primary heat exchanger: 2MW of the 3MW waste heat from the flow battery is used to preheat the CAES intake air, with the goal of heating it from 25℃ to 80℃, and the remaining 1MW of heat is used for subsequent processes.

[0089] Secondary heat exchanger: Receives the compression heat (approximately 2MW) generated by the primary compressor and transfers the heat to the phase change thermal storage module.

[0090] The controller outputs the following commands: Adjust the opening of the bypass three-way valve of the first-stage heat exchanger to 70%, so that 2MW of heat flow enters the heat exchanger and 1MW of heat flow bypasses.

[0091] Start the secondary heat exchanger circulation pump and set the flow rate to 2 m / s to ensure efficient heat transfer.

[0092] In this embodiment, the objective function is not directly generated by the LSTM model, but rather works in conjunction with the LSTM model, with the specific relationship as follows: The core function of the objective function: The objective function is a pre-defined optimization criterion used to quantify the core objectives of system operation (such as "maximizing thermal efficiency", "minimizing energy consumption", and "stabilizing battery temperature fluctuations"). (e.g., within 1℃). For example, the objective function during the charging phase might be: min(CAES compression power consumption + battery thermal management energy consumption), st40℃≤T≤55℃, Q thermal storage≥5MWh (constraints include battery temperature range, thermal storage requirements, etc.). The role of the LSTM model: As a prediction tool, the LSTM model provides key input data for solving the objective function, for example: Predict the changing trends of battery waste heat power and CAES intake flow rate within the next 15 minutes; Based on historical data, the heat loss and resistivity evolution of phase change thermal storage modules over time are predicted.

[0093] Collaborative process: The predicted data output by the LSTM model (such as future heat load and equipment status changes) is substituted into the objective function, and the optimal solution is solved by optimization algorithms (such as gradient descent and particle swarm optimization), and finally the control commands such as heat flow distribution and pump flow rate are generated.

[0094] In short, the LSTM model provides predictive information, and the objective function makes optimization decisions based on this information. Together, they support the dynamic regulation of the system.

[0095] (III) Heat exchange and heat storage processes Primary heat exchange: The 2MW waste heat from the flow battery is transferred to the air in the CAES intake pipeline through the primary heat exchanger. After 15 minutes, the air temperature rises from 25°C to 78°C, close to the target of 80°C, and the compression power consumption is reduced by 13%.

[0096] Secondary heat exchange and storage: High-temperature air (2MW of heat) at 120℃ from the primary compressor outlet enters the secondary heat exchanger and exchanges heat with the phase change thermal storage module. After MgCl2•6H2O absorbs heat, its temperature gradually rises to 117℃ and it begins to melt. After 1 hour, the phase change progress s=0.5, i.e., 50% solid and 50% liquid, was monitored using electrical resistivity tomography (ERT) technology, and the cumulative stored heat was approximately 1.8MW•h.

[0097] (iv) Feedback, correction and adjustment LSTM predictions provide "future trend predictions" for thermal management, while feedback corrections achieve "current bias elimination + long-term model optimization." The closed-loop logic of the combination of the two is as follows: predict decision making implement Detection deviation Modify model / strategy Further prediction; By supplementing model parameter correction and strategy iteration, the system has been upgraded from "passively adjusting actuators" to "actively optimizing the entire process," making it suitable for high-reliability energy storage systems.

[0098] When a deviation is detected, the actuator (such as pump speed) is adjusted to eliminate the immediate error. If the deviation occurs repeatedly, the model parameters are recalibrated and the prediction algorithm is optimized to form a long-term correction.

[0099] The controller compares the predicted values ​​with the actual monitoring data as follows: It was discovered that the CAES inlet temperature was below 80℃, with a deviation of 2℃. The flow rate of the primary heat exchanger circulation pump was immediately increased from 2m / s to 2.2m / s.

[0100] An abnormal local resistivity was detected in the phase change thermal storage module. This was addressed using a phase change progress-resistivity mapping model. Calculations revealed an excessively high proportion of solids in some areas, prompting an instruction to increase the heat flux density of the secondary heat exchanger to 2000 W / m². 2 This promotes a uniform phase transition.

[0101] II. Operation process during the discharge phase (a) Data Acquisition and Initial State During the peak power generation period (6 PM - 10 PM), the system enters the discharge process. At this time, the initial states of each component are as follows: Phase change thermal energy storage module: in a completely liquid state, temperature 117℃, resistivity 10. .

[0102] Compressed air energy storage system: The air tank pressure is 10MPa, waiting to expand and generate electricity.

[0103] Vanadium redox flow battery: prepared to receive waste heat for temperature control, electrolyte temperature 43℃.

[0104] The sensor collected the following data: Phase change thermal storage module resistivity sensor: 10 ; Gas tank pressure sensor: 10MPa; Electrolyte temperature sensor for flow battery: 43℃.

[0105] (II) Algorithm Decision Making and Heat Flow Allocation The objective function for predicting grid demand using the LSTM model is calculated as follows: Secondary heat exchanger: Releases 3.5MW of heat to heat the expander intake air from room temperature to 200℃.

[0106] Three-stage heat exchanger: recovers waste heat from the expander exhaust (0.5MW) to maintain the electrolyte temperature of the flow battery at 50℃.

[0107] The controller outputs the following commands: Adjust the three-way valve of the secondary heat exchanger to ensure that all the heat flow enters the expander intake pipe.

[0108] Start the three-stage heat exchanger circulation pump, set the flow rate to 1.5 m / s, and direct the waste heat to the liquid flow battery temperature control box.

[0109] (III) Heat exchange and heat release operation Secondary heat exchange (heat release): Phase change material (liquid) solid state, s from 1 0) Heat is released, and the air temperature rises from 25°C. At 200℃, the expander's power generation efficiency increases by 15%, with an output power of 10MW; Three-stage heat exchange (waste heat recovery): Expander exhaust (80℃) Heating the battery electrolyte at 60℃ (43℃) 50℃), waste heat utilization rate 95%, battery temperature stabilized at 50℃. 1℃.

[0110] (iv) Feedback, correction and adjustment 1. Controller monitoring and deviation identification Expander inlet temperature: fluctuations were detected. 4℃ (target) (3℃), indicating that the temperature control deviation exceeds the threshold; Flow battery temperature: Electrolyte temperature reached 51°C (target 50°C), indicating that thermal management did not meet expectations.

[0111] 2. Actuator real-time adjustment (basic "adjustment" action) Expander side: Fine-tune the heat flow rate of the secondary heat exchanger (e.g., from 50m³ / h). 3 / h 52m 3 / h), quickly pull the temperature back to 200±3℃; Battery side: Instruct the three-stage heat exchanger circulation pump to increase its speed (from 1.5 m / s). (1.8m / s), to enhance heat dissipation and reduce electrolyte temperature.

[0112] 3. Supplementing the correction process (the core value of "correction" is to make the system more intelligent) Model parameter correction (long-term adaptation material / equipment aging); If similar temperature deviations occur three times in a row, the LSTM prediction model parameters are iterated (the model weights are retrained using newly collected temperature fluctuation data) to make subsequent predictions more accurate. At the same time, the phase change progress-resistance value mapping model (if applicable) should be recalibrated for material thermal properties (such as thermal conductivity drift due to aging) to avoid long-term error accumulation.

[0113] Control strategy correction (short-term adaptation to changes in operating conditions): When the flow battery frequently overheats, the "battery overheat compensation coefficient" is automatically added to the heat flow distribution strategy (e.g., prioritizing the allocation of 10% of the additional heat dissipation power to the battery circuit). If the expander temperature fluctuates repeatedly, adjust the flow-temperature control curve of the secondary heat exchanger (e.g., from linear regulation). (Piecewise nonlinear compensation) to improve anti-interference capability.

[0114] 4. Revised closed-loop verification After adjustments and corrections, continuously monitor system operation for 30 minutes: Verify whether the expander intake temperature fluctuation is stable. Within 3℃; Verify whether the battery electrolyte temperature can be maintained at 50°C over a long period of time. 1℃; If the deviation persists, repeat the "adjustment" process. Correction The "verification" process continues until the system is adapted to the operating conditions.

[0115] Specifically, the controller detected: Expander inlet temperature fluctuation reached 4℃, exceeding the target Within a 3°C range, immediately fine-tune the heat flow rate of the secondary heat exchanger to stabilize the temperature at 200°C. 3℃; When the electrolyte temperature of the flow battery reaches 51℃, the flow rate of the circulating pump in the three-stage heat exchanger is increased to 1.8m / s to enhance heat dissipation and reduce the temperature to 50℃.

[0116] Through the operation of this embodiment, the present disclosure achieves the following performance improvements: The overall cycle efficiency has increased from 55% to 63% in the traditional system, and the annual power generation has increased by about 800,000 kWh.

[0117] Electrolyte temperature fluctuation control of flow batteries At 1℃, the cycle life is extended to 12 years, which is 3 years longer than the traditional system.

[0118] After 1000 cycles, the phase change thermal storage module showed only a 3% performance degradation, verifying the effectiveness of the nano-modified materials and the dynamically corrected phase change progress-resistance value mapping model.

[0119] The above embodiments fully demonstrate the entire process of this disclosure, from data acquisition and algorithm decision-making to device execution and feedback correction, reflecting the efficiency and stability of this disclosure in practical applications.

[0120] like Figure 5 As shown, this disclosure also provides a dynamic thermal management system for a cascade thermal coupling control system, the system comprising: The judgment module is used to determine the current operating status of the cascade thermal coupling control system; The operating parameter module is used to acquire the operating parameters of the cascade thermal coupling control system in real time based on the operating status. The actual heat flux value module is used to adjust the heat flux path according to the operating parameters to obtain the actual heat flux value; The heat flux difference module is used to obtain the predicted heat flux value using the heat flux prediction model and compare it with the actual heat flux value to obtain the heat flux difference. The adjustment module is used to adjust the operating parameters of the cascade thermal coupling control system according to the heat flow difference, thereby controlling the dynamic heat flow of the cascade thermal coupling control system.

[0121] Specifically, the actual heat flux value module is used to adjust the heat flux path according to operating parameters to obtain the actual heat flux value, including: The actual heat flow value module is used to construct the objective function, input the operating parameters, and obtain the heat flow command; Adjust the heat flow path according to the heat flow command to obtain the actual heat flow value.

[0122] Specifically, an objective function is constructed, and operating parameters are input to obtain heat flow instructions, including: Constructing the objective function based on LSTM neural network; By inputting the operating parameters into the objective function, the heat required for each path of the cascade thermal coupling control system is obtained; Generate heat flow instructions for each path based on the heat required for each path.

[0123] Specifically, the adjustment module is used to adjust the operating parameters of the cascade thermal coupling control system based on the heat flow difference, thereby controlling the dynamic heat flow of the cascade thermal coupling control system, including: The adjustment module is used to update the phase change progress-resistance value mapping model parameters based on the heat flow difference, and obtain a new phase change progress-resistance value mapping model. The operating parameters of the cascade thermal coupling control system are adjusted by using a new phase change progress-resistance value mapping model. The heat flow distribution of the cascade thermal coupling control system is dynamically managed based on the adjusted operating parameters.

[0124] Those skilled in the art should understand that, despite the detailed description of this disclosure with reference to the foregoing embodiments, modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this disclosure.

Claims

1. A dynamic thermal management method for a cascade thermal coupling control system, characterized in that, The method includes: Determine the current operating status of the cascade thermal coupling control system; Based on the operating status, the operating parameters of the cascade thermal coupling control system are acquired in real time; Adjust the heat flow path according to the operating parameters to obtain the actual heat flow value; The predicted heat flow value is obtained by using a heat flow prediction model and compared with the actual heat flow value to obtain the heat flow difference. The operating parameters of the cascade thermal coupling control system are adjusted based on the heat flow difference to control the dynamic heat flow of the cascade thermal coupling control system.

2. The dynamic thermal management method for a cascade thermal coupling control system according to claim 1, characterized in that, The operating states include charging state and discharging state.

3. The dynamic thermal management method for a cascade thermal coupling control system according to claim 2, characterized in that, When the operating state is charging, the operating parameters of the cascade thermal coupling control system are acquired in real time, including at least: flow battery temperature, CAES intake pipe temperature, first-stage compressor outlet temperature and phase change material resistivity. When the operating state is in discharge state, the operating parameters of the cascade thermal coupling control system are acquired in real time, including at least: phase change material resistivity, gas storage tank pressure and flow battery temperature.

4. The dynamic thermal management method for a cascade thermal coupling control system according to claim 1, characterized in that, Adjust the heat flow path according to the operating parameters to obtain the actual heat flow value, including: Construct the objective function, input the running parameters, and obtain the heat flow command; Adjust the heat flow path according to the heat flow command to obtain the actual heat flow value.

5. The dynamic thermal management method for a cascade thermal coupling control system according to claim 4, characterized in that, Construct the objective function, input the running parameters, and obtain the heat flow command, including: Constructing the objective function based on LSTM neural network; By inputting the operating parameters into the objective function, the heat required for each path of the cascade thermal coupling control system is obtained; Generate heat flow instructions for each path based on the heat required for each path.

6. The dynamic thermal management method for a cascade thermal coupling control system according to claim 1, characterized in that, Adjusting the operating parameters of the cascade thermal coupling control system based on the heat flow difference, and controlling the dynamic heat flow of the cascade thermal coupling control system includes: The phase change progress-resistance value mapping model parameters are updated based on the heat flow difference to obtain a new phase change progress-resistance value mapping model; The operating parameters of the cascade thermal coupling control system are adjusted by using a new phase change progress-resistance value mapping model. The heat flow distribution of the cascade thermal coupling control system is dynamically managed based on the adjusted operating parameters.

7. A dynamic thermal management system for a cascade thermal coupling control system, characterized in that, The system includes: The judgment module is used to determine the current operating status of the cascade thermal coupling control system; The operating parameter module is used to acquire the operating parameters of the cascade thermal coupling control system in real time based on the operating status. The actual heat flux value module is used to adjust the heat flux path according to the operating parameters to obtain the actual heat flux value; The heat flux difference module is used to obtain the predicted heat flux value using the heat flux prediction model and compare it with the actual heat flux value to obtain the heat flux difference. The adjustment module is used to adjust the operating parameters of the cascade thermal coupling control system according to the heat flow difference, thereby controlling the dynamic heat flow of the cascade thermal coupling control system.

8. The dynamic thermal management system for a cascade thermal coupling control system according to claim 7, characterized in that, The actual heat flux value module is used to adjust the heat flux path according to operating parameters to obtain the actual heat flux value, including: The actual heat flow value module is used to construct the objective function, input the operating parameters, and obtain the heat flow command; Adjust the heat flow path according to the heat flow command to obtain the actual heat flow value.

9. The dynamic thermal management system for a cascade thermal coupling control system according to claim 8, characterized in that, Construct the objective function, input the running parameters, and obtain the heat flow command, including: Constructing the objective function based on LSTM neural network; By inputting the operating parameters into the objective function, the heat required for each path of the cascade thermal coupling control system is obtained; Generate heat flow instructions for each path based on the heat required for each path.

10. The dynamic thermal management system for a cascade thermal coupling control system according to claim 7, characterized in that, The adjustment module is used to adjust the operating parameters of the cascade thermal coupling control system based on the heat flux difference, thereby controlling the dynamic heat flux of the cascade thermal coupling control system, including: The adjustment module is used to update the phase change progress-resistance value mapping model parameters based on the heat flow difference, and obtain a new phase change progress-resistance value mapping model. The operating parameters of the cascade thermal coupling control system are adjusted by using a new phase change progress-resistance value mapping model. The heat flow distribution of the cascade thermal coupling control system is dynamically managed based on the adjusted operating parameters.

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