A method for coordinated hydrogen thermal management of solid-state hydrogen storage devices and fuel cells

Abstract

This invention discloses a hydrogen-thermal synergistic management method for a solid-state hydrogen storage device and a fuel cell, relating to the field of fuel cells. The method includes: acquiring fuel cell operating parameters, calculating heat generation power and hydrogen consumption rate, and calculating the hydrogen evolution heat power required by the solid-state hydrogen storage device; acquiring operating data of the fuel cell cooling loop and the solid-state hydrogen storage device heating loop, determining the fuel cell heating power and the solid-state hydrogen storage device cooling power, and calculating the heat absorption matching degree and heat dissipation matching degree; and optimizing the flow rates of the cooling loop and heating loop based on the heat absorption matching degree and the heat dissipation matching degree, respectively, and determining the updated cooling loop flow rate and the updated heating loop flow rate. This invention improves energy utilization efficiency, reduces energy waste, and avoids equipment damage due to overheating or overcooling, thus extending the service life of the solid-state hydrogen storage device and the fuel cell.

Description

Technical Field

[0001] This invention relates to the field of fuel cell technology, and more specifically to a method for the coordinated management of hydrogen heat in a solid-state hydrogen storage device and a fuel cell. Background Technology

[0002] As hydrogen energy becomes increasingly important in the energy sector, the combination of solid-state hydrogen storage devices and fuel cells has become an important way to efficiently utilize hydrogen energy.

[0003] However, existing hydrogen thermal management methods for solid-state hydrogen storage devices and fuel cells struggle to precisely match the heat generation of the fuel cell with the heat required for hydrogen evolution in the solid-state hydrogen storage device, resulting in low energy utilization efficiency. Furthermore, existing hydrogen thermal management methods lack flexibility in responding to changes in various operating parameters. When fuel cell operating parameters such as power demand and operating current change, the flow rates of the cooling and heating loops cannot be adjusted promptly and effectively, preventing optimal hydrogen thermal synergy management and consequently shortening the equipment's lifespan.

[0004] Therefore, how to achieve hydrogen-thermal synergistic management of solid-state hydrogen storage devices and fuel cells to improve energy utilization efficiency is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0005] In view of this, the present invention provides a hydrogen thermal synergy management method for solid-state hydrogen storage devices and fuel cells to solve the technical problems of uncoordinated thermal management and low energy utilization efficiency between existing solid-state hydrogen storage devices and fuel cells.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: This invention discloses a method for synergistic management of hydrogen heat in a solid-state hydrogen storage device and a fuel cell, comprising: The operating parameters of the fuel cell are obtained, and the heat production power of the fuel cell is calculated based on the operating parameters; the hydrogen consumption rate is calculated based on the operating parameters, and then the heat power required for hydrogen evolution of the solid hydrogen storage device is calculated. The operation data of the fuel cell cooling loop and the solid hydrogen storage device heating loop are obtained, and the heat exchanger evaluator is used to determine the heating power supplied by the fuel cell to the solid hydrogen storage device and the cooling power supplied by the solid hydrogen storage device to the fuel cell; the cooling loop and the heating loop are connected by a plate heat exchanger. The heat absorption matching degree is calculated based on the heating power and the heat demand power, and the heat dissipation matching degree is calculated based on the cooling power and the heat generation power. Based on the heat absorption matching degree and the heat dissipation matching degree, the flow rates of the cooling circulation loop and the heating circulation loop are optimized respectively, and the updated cooling circulation loop flow rate and the updated heating circulation loop flow rate are determined to achieve hydrogen-thermal synergistic management.

[0007] Furthermore, obtaining the operating parameters of the fuel cell includes: obtaining power demand parameters, and obtaining the operating parameters of the fuel cell based on the power demand parameters; The calculation of fuel cell heat production power includes: retrieving a heat production power predictor, inputting the operating parameters into the heat production power predictor, and obtaining the battery heat production power; the heat production power predictor is trained based on a sample battery operating parameter set and a sample battery heat production power set, which are obtained based on historical fuel cell heat production records.

[0008] Furthermore, the step of calculating the hydrogen consumption rate based on the operating parameters includes: extracting the operating current from the operating parameters and calculating the hydrogen consumption rate by combining it with the hydrogen utilization efficiency of the fuel cell. The calculation of the hydrogen evolution heat power of the solid hydrogen storage device includes: obtaining the solid hydrogen storage material properties of the solid hydrogen storage device, and calculating the hydrogen evolution heat power based on the solid hydrogen storage material properties and the hydrogen consumption rate.

[0009] Furthermore, the operating data of the cooling circulation loop includes: cooling circulation loop flow rate, inlet temperature, and outlet temperature; the operating data of the heating circulation loop includes: heating circulation loop flow rate, inlet temperature, and outlet temperature. The heat generation power of the battery and the operating data of the cooling cycle loop are input into the heat treatment branch of the heat exchange evaluator to obtain the heat supply power of the fuel cell to the solid hydrogen storage device. The hydrogen evolution heat demand and the operating data of the heating cycle are input into the refrigeration processing branch of the heat exchange evaluator to obtain the refrigeration power from the solid hydrogen storage device to the fuel cell.

[0010] Furthermore, the construction steps of the heat exchanger evaluator include: Obtain the cooling medium information of the cooling circulation loop, the heating medium information of the heating circulation loop, and the heat exchanger model of the plate heat exchanger; Using the heat exchanger model as a model constraint, retrieve a set of heat exchanger records of the same model. Using the cooling medium information and the heating medium information as dual-medium constraints, the heat exchange record set of the same model is filtered to obtain the target heat exchange record set, which includes the heat exchange record set of the heating end and the heat exchange record set of the cooling end. Based on the heat exchange record set at the heating end, a sample heating parameter set and a sample heating power set are constructed. Each sample heating parameter in the sample heating parameter set includes: the heat generation power of the sample battery and the operating data of the sample cooling circulation loop. Based on the heat exchange record set at the cooling end, a sample refrigeration parameter set and a sample refrigeration power set are constructed. Each sample refrigeration parameter in the sample refrigeration parameter set includes the sample hydrogen evolution heat demand power and the operating data of the sample heating cycle loop. A heating processing branch is trained based on the sample heating parameter set and the sample heating power set, and a cooling processing branch is trained based on the sample cooling parameter set and the sample cooling power set. The heating processing branch and the cooling processing branch are integrated to form the heat exchange evaluator.

[0011] Furthermore, the calculation of the heat absorption matching degree includes: determining the maximum value among the heating power and the heat demand power as the first reference power, and determining the minimum value as the first comparison power, calculating the ratio of the first comparison power to the first reference power, and obtaining the heat absorption matching degree; The calculation of heat dissipation matching degree includes: determining the maximum value among the cooling power and the heat generation power as the second reference power, and determining the minimum value as the second comparison power, calculating the ratio of the second comparison power to the second reference power, and obtaining the heat dissipation matching degree.

[0012] Furthermore, determining the updated cooling loop flow rate and the updated heating loop flow rate includes: Obtain the heat absorption matching threshold and the heat dissipation matching threshold, and establish a flow optimization search space; Within the flow optimization search space, multiple candidate flow combinations are randomly selected, each candidate flow combination including candidate cooling loop flow and candidate heating loop flow. Based on the candidate cooling loop flow rate and candidate heating loop flow rate of each candidate flow rate combination, a heat transfer simulation model is configured and heat transfer simulation is performed to obtain multiple candidate matching degree combinations. Each candidate matching degree combination includes simulated heat absorption matching degree and simulated heat dissipation matching degree. When there is a candidate matching degree combination among the multiple candidate matching degree combinations that simultaneously satisfies the heat absorption matching threshold and the heat dissipation matching threshold, the target matching degree combination is determined, and the candidate cooling loop flow rate and candidate heating loop flow rate corresponding to the target matching degree combination are obtained as the updated cooling loop flow rate and updated heating loop flow rate. When none of the candidate matching degree combinations among the multiple candidate matching degree combinations satisfies the heat absorption matching threshold and the heat dissipation matching threshold, the search space is iteratively searched for flow rate optimization until the updated cooling circulation loop flow rate and the updated heating circulation loop flow rate are determined.

[0013] Furthermore, the iterative search flow optimization of the search space includes: Based on the simulated heat absorption matching degree and simulated heat dissipation matching degree among the multiple candidate matching degree combinations, a comprehensive matching degree score for each candidate matching degree combination is obtained. Based on the comprehensive matching score, the optimization iteration direction is determined in the traffic optimization search space; The search space is iteratively searched according to the optimization direction until the flow rate of the cooling loop and the flow rate of the heating loop are determined.

[0014] Furthermore, the iterative search process also includes: Get the optimization time limit; When a candidate matching degree combination that simultaneously satisfies the heat absorption matching threshold and the heat dissipation matching threshold is found within the optimization time limit, the corresponding candidate matching degree combination is taken as the target matching degree combination. If no candidate matching degree combination that satisfies the heat absorption matching threshold and heat dissipation matching threshold is found within the optimization time limit, all candidate matching degree combinations that have been searched are evaluated and sorted, and the candidate matching degree combination with the highest evaluation score is selected as the target matching degree combination.

[0015] Furthermore, the step of evaluating and ranking all candidate matching degree combinations searched, and selecting the candidate matching degree combination with the highest evaluation score as the target matching degree combination, includes: Iterate through all the candidate matching degree combinations that have been searched, calculate the evaluation score of each candidate matching degree combination, and select the candidate matching degree combination with the highest evaluation score as the target matching degree combination. The calculation of the evaluation score for each candidate matching degree combination includes: Extract the heat absorption matching degree and heat dissipation matching degree to be evaluated from the candidate matching degree combinations; determine the first dimension score based on the heat absorption matching degree to be evaluated and the heat absorption matching threshold, and determine the second dimension score based on the heat dissipation matching degree to be evaluated and the heat dissipation matching threshold. The evaluation score is obtained by weighted summation of the scores in the first dimension and the second dimension.

[0016] As can be seen from the above technical solution, compared with the prior art, the present invention provides a method for the coordinated management of hydrogen heat in a solid-state hydrogen storage device and a fuel cell, which has the following beneficial effects: This invention provides a hydrogen-thermal synergistic management method for solid-state hydrogen storage devices and fuel cells. First, it calculates the heat generation power of the fuel cell and the hydrogen evolution heat requirement of the solid-state hydrogen storage device, providing a data foundation for subsequent thermal management. Second, it obtains the current flow rate by connecting two circulation loops through a plate heat exchanger, allowing real-time monitoring of the system's operating status. Then, it calculates the heating power, cooling power, heat absorption matching degree, and heat dissipation matching degree to comprehensively evaluate the system's heat exchange performance. Finally, based on the matching degree, it optimizes the flow rate, dynamically adjusting the dual-loop flow rate of the plate heat exchanger. This ensures that the heat generated by the fuel cell maximally meets the hydrogen evolution requirements of the solid-state hydrogen storage device, while the solid-state hydrogen storage device effectively dissipates heat for the fuel cell, achieving synergistic hydrogen-thermal management.

[0017] The above technical solutions improve energy utilization efficiency and reduce energy waste. When excess heat generated by the fuel cell can be fully utilized by the solid-state hydrogen storage device, heat loss is avoided. Simultaneously, the cooling effect of the solid-state hydrogen storage device better maintains the normal operating temperature of the fuel cell, reducing additional heat dissipation energy consumption. Regarding equipment lifespan, timely responses to changes in operating parameters prevent damage from overheating or overcooling, extending the lifespan of both the solid-state hydrogen storage device and the fuel cell. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention 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 only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0019] Figure 1 This is a schematic diagram of the overall process of the present invention.

[0020] Figure 2 This invention provides a schematic diagram of the process for obtaining the heating power supplied by the fuel cell to the solid hydrogen storage device and the cooling power supplied by the solid hydrogen storage device to the fuel cell.

[0021] Figure 3 This is a schematic diagram of the process for calculating heat dissipation matching degree and heat absorption matching degree provided by the present invention.

[0022] Figure 4 A schematic diagram of the process for determining the flow rate of the updated cooling circulation loop and the flow rate of the updated heating circulation loop provided by the present invention. Detailed Implementation

[0023] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0024] This invention discloses a method for the coordinated management of hydrogen heat in a solid-state hydrogen storage device and a fuel cell, such as... Figure 1 As shown, it includes: Obtain the operating parameters of the fuel cell, calculate the heat production power of the fuel cell based on the operating parameters; calculate the hydrogen consumption rate based on the operating parameters, and then calculate the heat power required for hydrogen evolution of the solid hydrogen storage device. The operation data of the fuel cell cooling loop and the solid hydrogen storage device heating loop are obtained, and the heat exchanger evaluator is used to determine the heating power supplied by the fuel cell to the solid hydrogen storage device and the cooling power supplied by the solid hydrogen storage device to the fuel cell; the cooling loop and the heating loop are connected by a plate heat exchanger. Calculate the heat absorption matching degree based on the heating power and the heat demand power, and calculate the heat dissipation matching degree based on the cooling power and the heat production power; Based on the heat absorption matching degree and heat dissipation matching degree, the flow rates of the cooling circulation loop and the heating circulation loop are optimized respectively, and the updated cooling circulation loop flow rate and the updated heating circulation loop flow rate are determined to achieve hydrogen-thermal synergistic management.

[0025] In one specific embodiment, obtaining the operating parameters of the fuel cell includes: obtaining power demand parameters, and obtaining the operating parameters of the fuel cell based on the power demand parameters; Calculating the heat production power of a fuel cell includes: retrieving the heat production power predictor, inputting the operating parameters into the heat production power predictor, and obtaining the heat production power of the cell; the heat production power predictor is trained based on the sample cell operating parameter set and the sample cell heat production power set, which are obtained based on historical fuel cell heat production records.

[0026] Specifically, in this embodiment, firstly, power demand parameters reflecting the working state and load requirements of the fuel cell are obtained, i.e., how the fuel cell operates under power demand. Based on the power demand parameters, combined with the characteristics and operating rules of the fuel cell, the fuel cell operating parameters are obtained. For example, the corresponding operating parameters can be determined by looking up a table based on the power demand parameters and the fuel cell performance parameter table; or the operating parameters can be calculated using a calculation formula based on the power demand parameters, the fuel cell power characteristic curve, and the efficiency model.

[0027] The power supply requirements parameters include the target output power, target output voltage, and target output current; the battery operating parameters include the battery's power, operating current, and operating voltage.

[0028] Secondly, a heat generation power predictor, pre-trained based on a set of sample battery operating parameters and a set of sample battery heat generation power, is retrieved. This predictor is trained using a large amount of historical fuel cell heat generation data, ensuring high accuracy and reliability. The obtained battery operating parameters are then input into the heat generation power predictor, which calculates and outputs the battery's heat generation power.

[0029] Furthermore, a heat generation power predictor is trained based on the sample battery operating parameter set and the sample battery heat generation power set, as follows: First, data preparation involves collecting operating data of fuel cells under different operating conditions, including sample cell operating parameters such as electrical power, operating current, and operating voltage. Simultaneously, the corresponding sample cell heat generation power is recorded, constructing a sample cell operating parameter set and a sample cell heat generation power set. The collected data is then preprocessed to remove outliers and noisy data.

[0030] Secondly, the model is constructed based on a neural network as the model architecture for the heat generation power predictor. The preprocessed sample battery operating parameters are used as input, and the sample battery heat generation power set is used as output to train the selected model. The number of nodes in the input layer equals the dimension of the input features. For example, if there are three features (battery power, operating current, and operating voltage), the input layer contains three nodes. One to three hidden layers are set, with the number of nodes in each layer adjusted experimentally (e.g., 64, 32, etc.). The ReLU activation function is used. The number of nodes in the output layer equals the estimated battery heat generation power. For example, if the evaluation time requires one node, the output layer generally does not use an activation function and directly outputs continuous values.

[0031] Finally, the model is trained, and its parameters are continuously adjusted to minimize the error between the predicted and actual values. Cross-validation is used to evaluate the trained model and verify its generalization ability and accuracy. In each training iteration, the Adam optimizer and mean squared error (MSE) loss function are used to construct the training framework, and the model parameters are adjusted using the gradient descent algorithm. The batch size is set to 32, the total number of training epochs is 50, and an early stopping mechanism with a patience of 5 is introduced. When the validation set loss does not decrease for 5 consecutive epochs, the training process is automatically terminated, resulting in a trained heat power predictor.

[0032] By taking the above steps, the heat generation power of the fuel cell and the heat demand power for hydrogen evolution of the solid hydrogen storage device are obtained, providing a data foundation for subsequent hydrogen-thermal synergistic management, thereby improving energy utilization efficiency and realizing hydrogen-thermal synergistic management of the solid hydrogen storage device and the fuel cell.

[0033] In one specific embodiment, the hydrogen consumption rate is calculated based on operating parameters, including: extracting the operating current from the operating parameters and combining it with the hydrogen utilization efficiency of the fuel cell to calculate the hydrogen consumption rate. Calculating the heat power required for hydrogen evolution in a solid-state hydrogen storage device includes: obtaining the properties of the solid hydrogen storage material in the solid-state hydrogen storage device, and calculating the heat power required for hydrogen evolution based on the properties of the solid hydrogen storage material and the hydrogen consumption rate.

[0034] Specifically, in this embodiment, firstly, the operating current is extracted from the battery operating parameters. The operating current is the actual operating current of the fuel cell under current operating conditions, reflecting the intensity of the electrochemical reaction in the fuel cell. The electron flow rate can be calculated from the operating current, thereby determining the theoretical hydrogen consumption. Finally, the actual hydrogen consumption rate is obtained by combining this with the hydrogen utilization efficiency.

[0035] Furthermore, after extracting the operating current, and combining it with the hydrogen utilization efficiency of the fuel cell, the hydrogen consumption rate is obtained by the formula: "Hydrogen consumption rate = Operating current ÷ (Electron charge × Number of electrons participating in the reaction × Hydrogen utilization efficiency)". Here, hydrogen utilization efficiency refers to the ratio of the actual amount of hydrogen utilized by the fuel cell to the theoretical amount of hydrogen consumed, typically between 80% and 95%.

[0036] Secondly, the properties of the solid-state hydrogen storage materials for the solid-state hydrogen storage device are obtained. These properties include material type (e.g., rare-earth-based LaNi5, titanium-based TiFe, magnesium-based MgH2), dehydrogenation enthalpy change, hydrogen evolution activation energy, and operating temperature range, among other key thermodynamic parameters. Different types of hydrogen storage materials exhibit different hydrogen evolution characteristics. For example, LaNi5 alloys can evolve hydrogen at room temperature, while MgH2 requires a high temperature of 250-350℃ for effective hydrogen evolution. Based on the properties of the solid-state hydrogen storage materials and the hydrogen consumption rate, the heat power required for hydrogen evolution in the solid-state hydrogen storage device is calculated. Considering the thermodynamic characteristics of the dehydrogenation reaction, the hydrogen evolution process is endothermic, requiring a continuous supply of heat to maintain stable hydrogen production. The heat power required for hydrogen evolution is directly proportional to the hydrogen consumption rate, with the proportionality coefficient depending on the dehydrogenation enthalpy change of the storage material. Based on the properties of the solid-state hydrogen storage materials and the hydrogen consumption rate, the heat requirement of the solid-state hydrogen storage device under specific operating conditions is obtained.

[0037] For example, when the solid hydrogen storage material is a metal hydride, its hydrogen evolution heat power can be calculated using the formula "hydrogen evolution heat power = hydrogen consumption rate × heat of hydrogen absorption reaction of metal hydride". Assuming that the solid hydrogen storage device uses a magnesium-based hydrogen storage material with a hydrogen absorption reaction heat of 75 kJ / mol, and if the calculated hydrogen consumption rate is 0.01 mol / s, then the hydrogen evolution heat power of the solid hydrogen storage device is 0.75 kW.

[0038] By taking the above steps, the heat power required for hydrogen evolution of the solid hydrogen storage device can be obtained, providing a data foundation for realizing the hydrogen-thermal synergistic management of the solid hydrogen storage device and the fuel cell, which helps to improve energy utilization efficiency and reduce energy consumption.

[0039] In one specific embodiment, the operating data of the cooling circulation loop includes: cooling circulation loop flow rate, inlet temperature, and outlet temperature; the operating data of the heating circulation loop includes: heating circulation loop flow rate, inlet temperature, and outlet temperature. Input the battery heat generation power and the operating data of the cooling cycle loop into the heat treatment branch of the heat exchange evaluator to obtain the heat supply power of the fuel cell to the solid hydrogen storage device. The heat demand for hydrogen evolution and the operating data of the heating cycle are input into the refrigeration processing branch of the heat exchanger to obtain the refrigeration power from the solid hydrogen storage device to the fuel cell.

[0040] Specifically, a plate heat exchanger is used to connect the cooling loop of the fuel cell and the heating loop of the solid-state hydrogen storage device, achieving a key physical connection for hydrogen-thermal synergistic management. After the connection is completed, flow sensors are used to acquire the current flow rates of the cooling and heating loops. These flow sensors are characterized by high accuracy and high reliability, enabling real-time and accurate measurement of fluid flow rates in the loops.

[0041] Furthermore, the current cooling loop flow rate reflects the flow velocity of the coolant in the fuel cell cooling system. A suitable flow rate ensures that the heat generated by the fuel cell is carried away in a timely manner, maintaining its normal operating temperature. Conversely, the current heating loop flow rate reflects the flow of the heating medium in the solid-state hydrogen storage device's heating system. Sufficient flow rate ensures that the solid-state hydrogen storage device receives enough heat to meet the thermal requirements of its hydrogen evolution process.

[0042] The dual-loop design ensures the directionality and controllability of heat transfer while avoiding direct contact between different media. Real-time acquisition of flow parameters provides accurate input data for subsequent heat transfer power calculations and optimization decisions, which is a prerequisite for achieving precise control.

[0043] In the embodiments of this application, such as Figure 2 As shown, firstly, temperature sensors are used to acquire real-time temperature parameters on both sides of the plate heat exchanger. Temperature sensors are installed at the inlet and outlet of the cooling circulation loop, as well as at the inlet and outlet of the heating circulation loop, to ensure accurate temperature measurement. The inlet and outlet temperatures of the cooling circulation loop reflect the temperature change of the coolant as it enters and leaves the plate heat exchanger, while the inlet and outlet temperatures of the heating circulation loop reflect the temperature change of the heating medium within the plate heat exchanger.

[0044] Secondly, the pre-trained heat exchange evaluator is activated. The heat exchange evaluator is trained based on a large amount of historical data and experimental results, and includes a heating processing branch and a cooling processing branch, which are used to calculate the heating power supplied by the fuel cell to the solid hydrogen storage device and the cooling power supplied by the solid hydrogen storage device to the fuel cell, respectively.

[0045] Specifically, when calculating the heating power, the previously obtained battery heat generation power, the current cooling loop flow rate, and the inlet and outlet temperatures of the cooling loop are input into the heating processing branch. The heating processing branch, combined with the heat transfer characteristics and efficiency of the plate heat exchanger, obtains the heating power supplied by the fuel cell to the solid-state hydrogen storage device.

[0046] When calculating the cooling power, the heat power required for hydrogen evolution of the solid-state hydrogen storage device, the current flow rate of the heating loop, and the inlet and outlet temperatures of the heating loop are input into the cooling processing branch. The cooling processing branch also considers the relevant characteristics of the plate heat exchanger to calculate the cooling power from the solid-state hydrogen storage device to the fuel cell.

[0047] Through the above steps, the heating power supplied by the fuel cell to the solid hydrogen storage device and the cooling power supplied by the solid hydrogen storage device to the fuel cell are obtained. This provides a data foundation for further optimization of flow distribution and the realization of more efficient hydrogen-thermal synergistic management. As a result, the flow rate of the circulation loop can be adjusted according to actual needs, improving energy utilization efficiency, reducing energy consumption, and achieving stable and efficient operation of the solid hydrogen storage device and the fuel cell.

[0048] In one specific embodiment, the construction steps of the heat exchanger evaluator include: Obtain information on the cooling medium of the cooling circulation loop, the heating medium of the heating circulation loop, and the heat exchanger model of the plate heat exchanger; Using the heat exchanger model as a model constraint, retrieve the set of heat exchanger records of the same model. Using cooling medium information and heating medium information as dual medium constraints, the heat exchange record sets of the same model are filtered to obtain the target heat exchange record set, which includes the heat exchange record set of the heating end and the heat exchange record set of the cooling end. Based on the heat exchange record set at the heating end, a sample heating parameter set and a sample heating power set are constructed. Each sample heating parameter in the sample heating parameter set includes: sample battery heat generation power and sample cooling loop operation data. Based on the heat exchange record set at the cooling end, a sample cooling parameter set and a sample cooling power set are constructed. Each sample cooling parameter in the sample cooling parameter set includes the sample hydrogen evolution heat demand power and the operating data of the sample heating cycle loop. A heating processing branch is generated based on the sample heating parameter set and sample heating power set, and a cooling processing branch is generated based on the sample cooling parameter set and sample cooling power set. The heating processing branch and the cooling processing branch are integrated to form a heat exchange evaluator.

[0049] Specifically, in this embodiment, firstly, the cooling circuit medium of the cooling loop and the heating circuit medium of the heating loop are obtained, as well as the heat exchanger model of the plate heat exchanger. The cooling circuit medium is usually a coolant, while the heating circuit medium may be hot water. Different media have different thermophysical properties, such as specific heat capacity and thermal conductivity. The model of the plate heat exchanger determines its heat transfer area, structural form, and other parameters. Different models of heat exchangers differ in heat transfer efficiency, pressure loss, and other aspects.

[0050] Secondly, using the heat exchanger model as a model constraint, a set of heat exchanger records of the same model is retrieved. The model constraint filters out heat exchanger records of the same model as the currently used plate heat exchanger, and these records contain operating data of that model heat exchanger under different operating conditions.

[0051] Then, using both the cooling circuit medium and the heating circuit medium as dual-medium constraints, the heat exchange record sets of the same model are filtered to obtain the target heat exchange record set, which includes the heating end heat exchange record set and the cooling end heat exchange record set. Different media combinations will lead to different heat exchange effects. By using dual-medium constraints, heat exchange records that match the currently used cooling circuit medium and heating circuit medium can be further filtered out, improving the relevance and effectiveness of the data.

[0052] Subsequently, a sample heating parameter set and a sample heating power set were constructed based on the heat exchange record set at the heating end. Each sample heating parameter in the sample heating parameter set includes the sample battery heat generation power, the current flow rate of the sample cooling circulation loop, the sample inlet temperature of the cooling circulation loop, and the sample outlet temperature.

[0053] Simultaneously, a sample refrigeration parameter set and a sample refrigeration power set are constructed based on the heat exchange record set at the refrigeration end. Each sample refrigeration parameter in the sample refrigeration parameter set includes the sample hydrogen evolution heat power, the current flow rate of the sample heating loop, the sample inlet temperature of the heating loop, and the sample outlet temperature.

[0054] Finally, a heating processing branch is trained based on the sample heating parameter set and sample heating power set, and a cooling processing branch is trained based on the sample cooling parameter set and sample cooling power set. These two branches are then integrated to form a heat exchange evaluator. During training, a neural network is used to continuously adjust the model's parameters to improve the accuracy and reliability of the heating and cooling processing branches.

[0055] For example, a heat transfer estimator is built and trained based on a neural network, and the specific steps are as follows: First, data preparation involves collecting sample heating parameter sets, sample heating power sets, sample cooling parameter sets, and sample cooling power sets, and then preprocessing them. Preprocessing includes data cleaning to remove erroneous or abnormal data; and data normalization to unify data from different ranges to the same scale, such as normalizing temperature and flow rate data to the [0,1] interval, in order to accelerate model training and improve stability.

[0056] Next, the model is constructed, building a neural network model with an input layer, hidden layers, and an output layer. The number of nodes in the input layer is determined based on the number of features in the sample heating parameter set and the sample cooling parameter set. For example, if the sample heating parameter set has four features: sample battery heat generation power, sample current cooling loop flow rate, sample inlet temperature of the cooling loop, and sample outlet temperature, the input layer is set to 4 nodes. The number of nodes in the output layer is determined based on the number of outputs in the sample heating power set and the sample cooling power set, typically with 1 node in each. One to three hidden layers are set, with the number of nodes in each layer adjusted experimentally, such as 64 or 32. The activation function is ReLU, and the number of nodes is adjusted based on experience and experiments to balance the model's complexity and performance.

[0057] Secondly, for model training, the preprocessed data is divided into training, validation, and test sets according to a certain ratio, for example, 70% of the data is used as the training set, 15% as the validation set, and 15% as the test set. The neural network model is trained using the training set, and the model's weights and biases are continuously adjusted through backpropagation to minimize the error between predicted and actual values. During training, the Adam optimizer and the mean squared error (MSE) loss function are used to construct the training framework, and the model parameters are adjusted using the gradient descent algorithm. The batch size is set to 32, the total number of training rounds is 50, and an early stopping mechanism with a patience of 5 is introduced. When the validation set loss does not decrease for 5 consecutive rounds, the training process is automatically terminated. The heating and cooling branches are integrated to obtain the trained heat exchange evaluator.

[0058] Finally, model evaluation is performed. The trained neural network model is evaluated using a test set, and evaluation metrics such as mean squared error and mean absolute error are calculated. These metrics reflect the predictive accuracy and generalization ability of the heat exchanger estimator. If the evaluation metrics meet the requirements, the model is adopted as the final heat exchanger estimator; otherwise, the model's structure or parameters need to be adjusted, and it needs to be retrained and evaluated until satisfactory results are obtained.

[0059] Based on the heat exchange evaluator constructed above, the heating power supplied by the fuel cell to the solid hydrogen storage device and the cooling power supplied by the solid hydrogen storage device to the fuel cell can be calculated more accurately, providing more reliable data support for the hydrogen-thermal synergistic management of the solid hydrogen storage device and the fuel cell.

[0060] In one specific embodiment, calculating the heat absorption matching degree includes: determining the maximum value among the heating power and the heat demand power as a first reference power, and determining the minimum value as a first comparison power, calculating the ratio of the first comparison power to the first reference power, and obtaining the heat absorption matching degree; The heat dissipation matching degree is calculated by: determining the maximum value among cooling power and heat generation power as the second reference power, and determining the minimum value as the second comparison power, calculating the ratio of the second comparison power to the second reference power, and obtaining the heat dissipation matching degree.

[0061] In this embodiment, the heat absorption matching degree measures the extent to which the solid-state hydrogen storage device absorbs heat from the fuel cell to meet the heat demand for hydrogen evolution. The heat supply power is compared with the heat demand for hydrogen evolution, and the heat absorption matching degree is calculated using a formula, such as "Heat absorption matching degree = Heat supply power / Heat demand for hydrogen evolution power". When the heat absorption matching degree is close to 1, it indicates that the heat provided by the fuel cell can well meet the heat demand for hydrogen evolution in the solid-state hydrogen storage device, and the energy utilization is relatively efficient. If the heat absorption matching degree is much less than 1, it means that the solid-state hydrogen storage device may not be able to obtain enough heat, which will affect the hydrogen evolution efficiency. If the heat absorption matching degree is much greater than 1, it means that there is excess heat, resulting in energy waste.

[0062] Heat dissipation matching degree assesses the ability of a solid-state hydrogen storage device to dissipate heat from a fuel cell. It compares the cooling power with the heat generation power of the fuel cell, calculated using the formula: "Heat dissipation matching degree = Cooling power / Fuel cell heat generation power". When the heat dissipation matching degree is close to 1, it indicates that the solid-state hydrogen storage device can effectively remove the heat generated by the fuel cell, ensuring the fuel cell operates at a suitable temperature. If the heat dissipation matching degree is less than 1, the heat generated by the fuel cell may not be dissipated in time, leading to temperature increases and affecting its performance and lifespan. If the heat dissipation matching degree is greater than 1, it indicates that the cooling capacity of the solid-state hydrogen storage device is too strong, which may also lead to the inefficient use of energy.

[0063] By calculating the heat absorption matching degree and heat dissipation matching degree, we can understand the matching of heat exchange between fuel cells and solid hydrogen storage devices, providing a basis for subsequent adjustment of circulation loop flow and optimization of hydrogen-thermal synergistic management strategies.

[0064] like Figure 3 As shown, the specific calculation steps include: The maximum value between the heating power and the hydrogen evolution heat demand power is determined as the first reference power, and the minimum value is determined as the first comparison power. The ratio of the first comparison power to the first reference power is calculated to obtain the endothermic matching degree. The maximum value between the cooling power and the battery heat generation power is determined as the second reference power, and the minimum value is determined as the second comparison power. The ratio of the second comparison power to the second reference power is calculated to obtain the heat dissipation matching degree.

[0065] First, the maximum value between the heating power and the heat required for hydrogen evolution is determined by comparison, serving as the first reference power and the minimum value as the first comparison power. For example, if the heating power is greater than the heat required for hydrogen evolution, then the heating power is the first reference power, and the heat required for hydrogen evolution is the first comparison power; conversely, if the heating power is less than the heat required for hydrogen evolution, then the heating power is the first reference power, and the heat required for hydrogen evolution is the first comparison power; the reverse is also true.

[0066] Then, the ratio of the first comparative power to the first reference power is calculated as the heat absorption matching degree. This ratio reflects the degree of matching between the heat provided by the fuel cell and the heat required for hydrogen evolution in the solid-state hydrogen storage device. If the ratio is close to 1, it indicates that the two are well matched and the energy utilization efficiency is high.

[0067] For example, assuming the heating power is 100kW and the heat power required for hydrogen evolution is 90kW, then the first reference power is 100kW, the first comparison power is 90kW, and the heat absorption matching degree is 90÷100=0.9, indicating that the heat provided by the fuel cell is well matched with the heat required for hydrogen evolution by the solid hydrogen storage device, and the energy utilization efficiency is relatively high.

[0068] Similarly, a similar method is used for cooling power and battery heat generation power. First, the two are compared to determine the maximum value as the second reference power and the minimum value as the second comparative power. For example, when the cooling power is greater than the battery heat generation power, the cooling power is the second reference power, and the battery heat generation power is the second comparative power. Next, the ratio of the second comparative power to the second reference power is calculated to obtain the heat dissipation matching degree. This ratio reflects the matching status between the solid-state hydrogen storage device's ability to dissipate heat from the fuel cell and the fuel cell's heat generation. When the ratio is close to 1, it indicates that the solid-state hydrogen storage device can effectively dissipate heat from the fuel cell, ensuring its stable operation at a suitable temperature.

[0069] By calculating the endothermic and heat dissipation matching degrees using the above methods, we can understand the matching of heat exchange between the fuel cell and the solid hydrogen storage device. This provides an accurate and reliable basis for further optimizing the circulation loop flow and improving the hydrogen-thermal synergistic management strategy, thereby achieving efficient and stable operation of the solid hydrogen storage device and the fuel cell system, improving the energy utilization efficiency of the entire energy system, and reducing energy consumption.

[0070] In one specific embodiment, determining the update of the cooling loop flow rate and the update of the heating loop flow rate includes: Obtain the heat absorption matching threshold and the heat dissipation matching threshold, and establish a flow optimization search space; Within the flow optimization search space, multiple candidate flow combinations are randomly selected, each candidate flow combination including candidate cooling loop flow and candidate heating loop flow. Based on the candidate cooling loop flow rate and candidate heating loop flow rate of each candidate flow rate combination, heat transfer simulation models are configured and heat transfer simulations are performed to obtain multiple candidate matching degree combinations. Each candidate matching degree combination includes simulated heat absorption matching degree and simulated heat dissipation matching degree. When there is a candidate matching degree combination among multiple candidate matching degree combinations that simultaneously satisfies the heat absorption matching threshold and the heat dissipation matching threshold, the target matching degree combination is determined, and the candidate cooling loop flow rate and candidate heating loop flow rate corresponding to the target matching degree combination are obtained as the updated cooling loop flow rate and updated heating loop flow rate. When none of the candidate matching degree combinations among multiple candidate matching degree combinations satisfies the heat absorption matching threshold and the heat dissipation matching threshold, the search space is iteratively searched for flow optimization until the updated cooling loop flow rate and the updated heating loop flow rate are determined.

[0071] Specifically, the flow rates of the current cooling and heating loops are optimized based on the heat absorption and heat dissipation matching degrees. When the heat absorption matching degree is low, it indicates that the solid-state hydrogen storage device is not absorbing enough heat from the fuel cell to meet the heat requirements for hydrogen evolution. In this case, the flow rate of the heating loop should be appropriately increased to allow more heat to be transferred to the solid-state hydrogen storage device, thereby improving the hydrogen evolution efficiency. Conversely, if the heat absorption matching degree is too high, resulting in excess heat, the flow rate of the heating loop can be reduced to avoid energy waste.

[0072] Regarding the heat dissipation matching degree, if it is less than 1, it indicates that the solid-state hydrogen storage device's ability to dissipate heat for the fuel cell is insufficient. The fuel cell may experience temperature rise due to the inability to dissipate heat in time, affecting its performance and lifespan. In this case, increasing the flow rate of the cooling circulation loop can enhance the heat dissipation effect. Conversely, when the heat dissipation matching degree is greater than 1, the solid-state hydrogen storage device's cooling capacity is too strong, which may lead to the unreasonable use of energy. In this case, the flow rate of the cooling circulation loop should be appropriately reduced.

[0073] After obtaining the updated flow rate, it is configured into the dual loop of the plate heat exchanger. As a key device for heat exchange, the proper configuration of the dual loop flow rate in the plate heat exchanger directly affects the effectiveness of hydrogen-thermal synergistic management. During the configuration process, it is ensured that flow rate adjustments will not affect system stability, while also considering factors such as potential pressure losses due to flow rate changes.

[0074] By optimizing and configuring the flow rate, the heat exchange between the solid-state hydrogen storage device and the fuel cell can be made more rational, improving energy utilization efficiency, reducing energy consumption, and achieving stable and efficient operation of both, thus providing a strong guarantee for the reliable operation of the entire energy system.

[0075] In this embodiment, firstly, pre-set heat absorption matching thresholds and heat dissipation matching thresholds are obtained. These two thresholds can measure whether the degree of heat exchange matching meets the standards. Simultaneously, a flow optimization search space is established, which limits the value range of candidate cooling loop flow rates and candidate heating loop flow rates, ensuring that the search process is conducted within a reasonable parameter range.

[0076] Secondly, multiple candidate flow combinations are randomly selected within the flow optimization search space. Each candidate flow combination consists of candidate cooling loop flow and candidate heating loop flow. This random selection method can, to some extent, ensure the comprehensiveness of the search and avoid getting trapped in local optima.

[0077] Then, heat transfer simulation models were configured and simulations were performed based on the candidate cooling loop flow rate and candidate heating loop flow rate for each candidate flow rate combination. The heat transfer simulation model is a mathematical simulation of the actual heat transfer process. By inputting different flow rate parameters, it simulates the heat exchange under different flow rate conditions, thereby obtaining multiple candidate matching degree combinations. Each candidate matching degree combination includes simulated endothermic matching degree and simulated heat dissipation matching degree, reflecting the degree of matching of heat exchange between the solid-state hydrogen storage device and the fuel cell under the corresponding candidate flow rate combination.

[0078] Next, the multiple candidate matching degree combinations are evaluated. When a candidate matching degree combination satisfies both the endothermic and exothermic matching thresholds, a suitable flow combination has been found. This candidate matching degree combination is then designated as the target matching degree combination, and the corresponding candidate cooling and heating loop flow rates are obtained and used as updated cooling and heating loop flow rates. These two updated flow rates will be used to configure the dual loop of the plate heat exchanger to achieve more efficient hydrogen-thermal synergistic management.

[0079] Furthermore, when multiple candidate matching degree combinations satisfying the two thresholds exist, each satisfying candidate matching degree combination is weighted and evaluated according to a preset weight coefficient. The candidate matching degree combination with the highest weighted evaluation value is selected as the target matching degree combination. The specific weights can be customized by the user. For example, when heat dissipation and heat absorption are equally important, each weight is 0.5; when heat dissipation is given more importance, the heat dissipation weight can be set to 0.6. Weighted evaluation value = α × simulated heat absorption matching degree + β × simulated heat dissipation matching degree, where α + β = 1.

[0080] For example, if the user prioritizes heat dissipation, and sets the heat dissipation weight β to 0.6 and the heat absorption weight α to 0.4, there are three candidate matching degree combinations that satisfy both the heat absorption matching threshold and the heat dissipation matching threshold: Combination A: Simulated heat absorption matching degree is 0.8, and simulated heat dissipation matching degree is 0.9; Combination B: Simulated heat absorption matching degree is 0.9, and simulated heat dissipation matching degree is 0.8; Combination C: Simulated heat absorption matching degree is 0.85, and simulated heat dissipation matching degree is 0.85.

[0081] The weighted evaluation value of combination A is 0.4×0.8+0.6×0.9=0.86; The weighted evaluation value of combination B is 0.4 × 0.9 + 0.6 × 0.8 = 0.84; The weighted evaluation value of combination C is 0.4×0.85+0.6×0.85=0.85.

[0082] The comparison shows that combination A has the highest weighted evaluation value, so combination A is selected as the target matching combination. The corresponding candidate cooling loop flow rate and candidate heating loop flow rate are the updated cooling loop flow rate and updated heating loop flow rate.

[0083] However, if none of the candidate matching combinations satisfies the heat absorption and heat dissipation matching thresholds, it indicates that the currently randomly selected candidate flow combinations have failed to achieve the expected matching effect. In this case, an iterative search for flow optimization is initiated. During the iterative search, the search strategy is continuously adjusted, such as narrowing the search range or changing the random selection method, to find better candidate flow combinations until the required updated cooling and heating loop flow rates are determined.

[0084] By using an iterative search approach, the optimal solution can be gradually approximated, improving the accuracy and reliability of flow optimization, and ultimately achieving efficient collaborative management of heat exchange between solid-state hydrogen storage devices and fuel cells.

[0085] 8. A method for coordinated hydrogen thermal management of a solid-state hydrogen storage device and a fuel cell according to claim 7, characterized in that the iterative search flow optimization search space includes: Based on the simulated heat absorption matching degree and simulated heat dissipation matching degree among multiple candidate matching degree combinations, the comprehensive matching degree score of each candidate matching degree combination is obtained. Based on the comprehensive matching score, the direction of optimization iteration is determined in the traffic optimization search space; The search space is iteratively optimized by searching the flow rate in the direction of optimization until the flow rate of the cooling circulation loop and the flow rate of the heating circulation loop are determined.

[0086] Specifically, such as Figure 4 As shown in this embodiment, firstly, based on the simulated heat absorption matching degree and simulated heat dissipation matching degree among multiple candidate matching degree combinations, a comprehensive matching degree score for multiple candidate matching degree combinations is calculated. This comprehensive score can be calculated using a formula, such as "Comprehensive Matching Degree Score = γ × Simulated Heat Absorption Matching Degree + δ × Simulated Heat Dissipation Matching Degree," where γ and δ are weighting coefficients, and γ + δ = 1. These weighting coefficients can be adjusted according to actual needs and priorities. If more attention is paid to heat absorption matching, the value of γ can be set larger; if more emphasis is placed on heat dissipation matching, the value of δ can be increased.

[0087] Secondly, after obtaining multiple comprehensive matching scores, the optimization iteration direction is determined within the traffic optimization search space. This direction moves towards the direction with the highest comprehensive matching score. By comparing the comprehensive matching scores of adjacent regions, the direction of score increase is determined, thus identifying the optimization iteration direction. For example, the traffic optimization search space is divided into multiple small regions, the comprehensive matching score of candidate matching combinations in each region is calculated, and then the scores of adjacent regions are compared to select the direction of score increase as the optimization iteration direction.

[0088] After determining the optimization iteration direction, the search space is optimized by iteratively searching the flow rate along that direction. During the iterative search, each search selects a new candidate flow rate combination along the optimization iteration direction, and a heat transfer simulation model is configured based on this new combination to perform heat transfer simulation, obtaining a new candidate matching degree combination and a comprehensive matching degree score. This process is repeated continuously, adjusting the candidate flow rate combinations until a candidate matching degree combination that satisfies both the heat absorption and heat dissipation matching thresholds is found. The corresponding candidate cooling loop flow rate and candidate heating loop flow rate at this point are the updated cooling loop flow rate and updated heating loop flow rate.

[0089] 9. A method for coordinated hydrogen thermal management of a solid-state hydrogen storage device and a fuel cell according to claim 7, characterized in that the iterative search process further includes: Get the optimization time limit; When a candidate matching degree combination that simultaneously satisfies the heat absorption matching threshold and the heat dissipation matching threshold is found within the optimization time limit, the corresponding candidate matching degree combination is taken as the target matching degree combination. If no candidate matching degree combination that satisfies the heat absorption matching threshold and heat dissipation matching threshold is found within the optimization time limit, all candidate matching degree combinations that have been searched are evaluated and ranked, and the candidate matching degree combination with the highest evaluation score is selected as the target matching degree combination.

[0090] Specifically, in this embodiment, firstly, a pre-set optimization time limit is obtained to avoid unlimited search process and ensure that optimization results are obtained within a certain time.

[0091] When the flow optimization search begins, a timer is started. Within the optimization time limit, candidate flow combinations are randomly selected, a heat transfer simulation model is configured for heat transfer simulation, a comprehensive matching score is calculated, and the optimization iteration direction is determined according to the above method, in order to find candidate matching combinations that meet the heat absorption matching threshold and the heat dissipation matching threshold.

[0092] If a candidate matching degree combination that satisfies both thresholds is successfully found within the optimization time limit, then this candidate matching degree combination is determined as the target matching degree combination. The candidate cooling loop flow rate and candidate heating loop flow rate corresponding to the target matching degree combination are obtained and used as the updated cooling loop flow rate and updated heating loop flow rate for subsequent dual-loop configuration of the plate heat exchanger, achieving hydrogen-thermal synergistic management.

[0093] However, if, within the optimization time limit, after multiple searches and iterations, no candidate matching degree combination satisfying the heat absorption matching threshold and the heat dissipation matching threshold is found, then all searched candidate matching degree combinations are evaluated and ranked. The weighted evaluation method mentioned earlier is used, that is, the simulated heat absorption matching degree and simulated heat dissipation matching degree in each candidate matching degree combination are weighted according to preset weight coefficients to obtain an evaluation score for each candidate matching degree combination. Then, the candidate matching degree combination with the highest evaluation score is selected as the target matching degree combination from all searched candidate matching degree combinations.

[0094] For example, the heat absorption threshold requirement is 0.8, and the heat dissipation threshold requirement is 0.8. The optimal solution selected is a heat absorption matching degree of 0.75 and a heat dissipation matching degree of 0.85. At this time, the relatively optimal flow configuration is adopted, and the auxiliary equipment is started to compensate for the insufficient part. In addition, for the insufficient heat absorption matching degree, the auxiliary electric heater of the solid hydrogen storage device is started to maintain the state where the heat dissipation matching degree is met and avoids additional heat dissipation burden.

[0095] Similarly, the candidate cooling loop flow rate and candidate heating loop flow rate corresponding to the target matching degree combination are obtained as the updated cooling loop flow rate and updated heating loop flow rate. Although the result may not be the optimal solution that completely satisfies the two thresholds, it is a relatively better choice under time constraints. It can improve the rationality of heat exchange and energy utilization efficiency between the solid hydrogen storage device and the fuel cell to a certain extent, and ensure the stable operation of the entire energy system.

[0096] By setting a time limit for optimization, it is possible to improve the system's response speed and operating efficiency while ensuring a certain optimization effect, and avoid affecting the real-time operation of the energy system due to a long search process.

[0097] In one specific embodiment, all candidate matching degree combinations searched are evaluated and ranked, and the candidate matching degree combination with the highest evaluation score is selected as the target matching degree combination, including: Iterate through all the candidate matching degree combinations that have been searched, calculate the evaluation score of each candidate matching degree combination, and select the candidate matching degree combination with the highest evaluation score as the target matching degree combination. Calculate the evaluation score for each candidate matching degree combination, including: Extract the heat absorption matching degree and heat dissipation matching degree to be evaluated from the candidate matching degree combinations; determine the first dimension score based on the heat absorption matching degree and heat absorption matching threshold, and determine the second dimension score based on the heat dissipation matching degree and heat dissipation matching threshold. The evaluation score is obtained by weighted summation of the first dimension score and the second dimension score.

[0098] Specifically, in this embodiment, firstly, all candidate matching degree combinations that have been searched are traversed to obtain the first candidate matching degree combination. The first candidate matching degree combination includes the simulated heat absorption matching degree and the simulated heat dissipation matching degree obtained in the previous heat exchange simulation process, which are defined as the first heat absorption matching degree to be evaluated and the first heat dissipation matching degree to be evaluated, respectively.

[0099] Secondly, the first dimension score is determined based on the first degree of heat absorption matching to be evaluated and a pre-set heat absorption matching threshold. This can be calculated using established scoring rules; for example, a higher base score is given when the first degree of heat absorption matching reaches or exceeds the heat absorption matching threshold, and a deduction is made based on the difference if it does not reach the threshold. Similarly, the second dimension score is determined based on the first degree of heat dissipation matching to be evaluated and the heat dissipation matching threshold.

[0100] Then, the scores from the first and second dimensions are weighted to obtain the first evaluation score. The weighting coefficients can be adjusted according to actual needs and priorities. If more attention is paid to heat absorption matching, the weight of the first dimension score can be set higher; if more attention is paid to heat dissipation matching, the weight of the second dimension score can be increased.

[0101] Then, following the same method used to obtain the first evaluation score of the first candidate matching degree combination, the remaining candidate matching degree combinations are processed sequentially to obtain their respective evaluation scores, ultimately resulting in multiple evaluation scores. For example, the evaluation score = α × min(simulated heat absorption matching degree / heat absorption matching threshold, 1) + β × min(simulated heat dissipation matching degree / heat dissipation matching threshold, 1).

[0102] Finally, multiple evaluation scores are compared, and the candidate matching combination with the highest evaluation score is selected as the target matching combination. The candidate cooling loop flow rate and candidate heating loop flow rate corresponding to this target matching combination are the updated cooling loop flow rate and updated heating loop flow rate.

[0103] For example, with α=0.4, β=0.6, an endothermic matching threshold of 0.8, and a heat dissipation matching threshold of 0.8, there are three candidate matching degree combinations: Combination D: Simulated heat absorption matching degree is 0.7, simulated heat dissipation matching degree is 0.9; Combination E: Simulated heat absorption matching degree is 0.85, and simulated heat dissipation matching degree is 0.7; Combination F: Simulated heat absorption matching degree is 0.75, and simulated heat dissipation matching degree is 0.8.

[0104] The evaluation score of combination D = 0.4 × min(0.7 / 0.8, 1) + 0.6 × min(0.9 / 0.8, 1) = 0.4 × 0.875 + 0.6 × 1 = 0.95; The evaluation score of combination E = 0.4 × min(0.85 / 0.8, 1) + 0.6 × min(0.7 / 0.8, 1) = 0.4 × 1 + 0.6 × 0.875 = 0.925; The evaluation score of combination F = 0.4 × min(0.75 / 0.8, 1) + 0.6 × min(0.8 / 0.8, 1) = 0.4 × 0.9375 + 0.6 × 1 = 0.975.

[0105] By comparison, combination F has the highest evaluation score, so combination F is selected as the target matching combination.

[0106] By evaluating and ranking numerous candidate matching combinations, the relatively optimal solution is selected, further optimizing the hydrogen-thermal synergistic management between the solid-state hydrogen storage device and the fuel cell, thereby improving energy utilization efficiency and system operational stability. Simultaneously, under limited optimization time, it can quickly find a relatively optimal result, balancing the relationship between optimization effectiveness and system response speed.

[0107] In summary, compared to existing technologies, this application utilizes a flow control strategy to transfer waste heat generated during fuel cell power generation to a solid-state hydrogen storage device for hydrogen evolution. Simultaneously, the low-temperature medium from hydrogen evolution in the storage device provides cooling for the fuel cell, achieving bidirectional heat recycling within the system. By dynamically adjusting the flow parameters of the circulation loops on both sides of the plate heat exchanger, the system's internal heat utilization efficiency is maximized, reducing the energy consumption of external auxiliary heating and cooling equipment, ultimately achieving energy saving and efficiency improvement for the hydrogen fuel cell system.

[0108] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.

[0109] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for coordinated hydrogen thermal management of a solid-state hydrogen storage device and a fuel cell, characterized in that, include: Obtain the operating parameters of the fuel cell, and calculate the heat output power of the fuel cell based on the operating parameters; The hydrogen consumption rate is calculated based on the operating parameters, and then the heat power required for hydrogen evolution of the solid hydrogen storage device is calculated. The operation data of the fuel cell cooling loop and the solid hydrogen storage device heating loop are obtained, and the heat exchanger evaluator is used to determine the heating power supplied by the fuel cell to the solid hydrogen storage device and the cooling power supplied by the solid hydrogen storage device to the fuel cell; the cooling loop and the heating loop are connected by a plate heat exchanger. The heat absorption matching degree is calculated based on the heating power and the heat demand power, and the heat dissipation matching degree is calculated based on the cooling power and the heat generation power. Based on the heat absorption matching degree and the heat dissipation matching degree, the flow rates of the cooling circulation loop and the heating circulation loop are optimized respectively, and the updated cooling circulation loop flow rate and the updated heating circulation loop flow rate are determined to achieve hydrogen-thermal synergistic management.

2. The method for coordinated hydrogen thermal management of a solid-state hydrogen storage device and a fuel cell according to claim 1, characterized in that, The process of obtaining the operating parameters of the fuel cell includes: obtaining power demand parameters, and obtaining the operating parameters of the fuel cell based on the power demand parameters; The calculation of fuel cell heat production power includes: retrieving a heat production power predictor, inputting the operating parameters into the heat production power predictor, and obtaining the battery heat production power; the heat production power predictor is trained based on a sample battery operating parameter set and a sample battery heat production power set, which are obtained based on historical fuel cell heat production records.

3. The method for coordinated hydrogen thermal management of a solid-state hydrogen storage device and a fuel cell according to claim 1, characterized in that, The step of calculating the hydrogen consumption rate based on the operating parameters includes: extracting the operating current from the operating parameters and calculating the hydrogen consumption rate by combining it with the hydrogen utilization efficiency of the fuel cell. The calculation of the hydrogen evolution heat power of the solid hydrogen storage device includes: obtaining the solid hydrogen storage material properties of the solid hydrogen storage device, and calculating the hydrogen evolution heat power based on the solid hydrogen storage material properties and the hydrogen consumption rate.

4. The method for coordinated hydrogen thermal management of a solid-state hydrogen storage device and a fuel cell according to claim 1, characterized in that, The operating data of the cooling circulation loop includes: cooling circulation loop flow rate, inlet temperature, and outlet temperature; the operating data of the heating circulation loop includes: heating circulation loop flow rate, inlet temperature, and outlet temperature. The heat generation power of the battery and the operating data of the cooling cycle loop are input into the heat treatment branch of the heat exchange evaluator to obtain the heat supply power of the fuel cell to the solid hydrogen storage device. The hydrogen evolution heat demand and the operating data of the heating cycle are input into the refrigeration processing branch of the heat exchange evaluator to obtain the refrigeration power from the solid hydrogen storage device to the fuel cell.

5. The method for coordinated hydrogen thermal management of a solid-state hydrogen storage device and a fuel cell according to claim 4, characterized in that, The construction steps of the heat exchanger evaluator include: Obtain the cooling medium information of the cooling circulation loop, the heating medium information of the heating circulation loop, and the heat exchanger model of the plate heat exchanger; Using the heat exchanger model as a model constraint, retrieve a set of heat exchanger records of the same model. Using the cooling medium information and the heating medium information as dual-medium constraints, the heat exchange record set of the same model is filtered to obtain the target heat exchange record set, which includes the heat exchange record set of the heating end and the heat exchange record set of the cooling end. Based on the heat exchange record set at the heating end, a sample heating parameter set and a sample heating power set are constructed. Each sample heating parameter in the sample heating parameter set includes: the heat generation power of the sample battery and the operating data of the sample cooling circulation loop. Based on the heat exchange record set at the cooling end, a sample refrigeration parameter set and a sample refrigeration power set are constructed. Each sample refrigeration parameter in the sample refrigeration parameter set includes the sample hydrogen evolution heat demand power and the operating data of the sample heating cycle loop. A heating processing branch is trained based on the sample heating parameter set and the sample heating power set, and a cooling processing branch is trained based on the sample cooling parameter set and the sample cooling power set. The heating processing branch and the cooling processing branch are integrated to form the heat exchange evaluator.

6. The method for coordinated hydrogen thermal management of a solid-state hydrogen storage device and a fuel cell according to claim 1, characterized in that, The calculation of the heat absorption matching degree includes: determining the maximum value among the heating power and the heat demand power as the first reference power, and determining the minimum value as the first comparison power, calculating the ratio of the first comparison power to the first reference power, and obtaining the heat absorption matching degree; The calculation of heat dissipation matching degree includes: determining the maximum value among the cooling power and the heat generation power as the second reference power, and determining the minimum value as the second comparison power, calculating the ratio of the second comparison power to the second reference power, and obtaining the heat dissipation matching degree.

7. The method for coordinated hydrogen thermal management of a solid-state hydrogen storage device and a fuel cell according to claim 1, characterized in that, The determination of the updated cooling loop flow rate and the updated heating loop flow rate includes: Obtain the heat absorption matching threshold and the heat dissipation matching threshold, and establish a flow optimization search space; Within the flow optimization search space, multiple candidate flow combinations are randomly selected, each candidate flow combination including candidate cooling loop flow and candidate heating loop flow. Based on the candidate cooling loop flow rate and candidate heating loop flow rate of each candidate flow rate combination, a heat transfer simulation model is configured and heat transfer simulation is performed to obtain multiple candidate matching degree combinations. Each candidate matching degree combination includes simulated heat absorption matching degree and simulated heat dissipation matching degree. When there is a candidate matching degree combination among the multiple candidate matching degree combinations that simultaneously satisfies the heat absorption matching threshold and the heat dissipation matching threshold, the target matching degree combination is determined, and the candidate cooling loop flow rate and candidate heating loop flow rate corresponding to the target matching degree combination are obtained as the updated cooling loop flow rate and updated heating loop flow rate. When none of the candidate matching degree combinations among the multiple candidate matching degree combinations satisfies the heat absorption matching threshold and the heat dissipation matching threshold, the search space is iteratively searched for flow rate optimization until the updated cooling circulation loop flow rate and the updated heating circulation loop flow rate are determined.

8. The method for coordinated hydrogen thermal management of a solid-state hydrogen storage device and a fuel cell according to claim 7, characterized in that, The iterative search flow optimization of the search space includes: Based on the simulated heat absorption matching degree and simulated heat dissipation matching degree among the multiple candidate matching degree combinations, a comprehensive matching degree score for each candidate matching degree combination is obtained. Based on the comprehensive matching score, the optimization iteration direction is determined in the traffic optimization search space; The search space is iteratively searched according to the optimization direction until the flow rate of the cooling loop and the flow rate of the heating loop are determined.

9. The method for coordinated hydrogen thermal management of a solid-state hydrogen storage device and a fuel cell according to claim 7, characterized in that, The iterative search process also includes: Get the optimization time limit; When a candidate matching degree combination that simultaneously satisfies the heat absorption matching threshold and the heat dissipation matching threshold is found within the optimization time limit, the corresponding candidate matching degree combination is taken as the target matching degree combination. If no candidate matching degree combination that satisfies the heat absorption matching threshold and heat dissipation matching threshold is found within the optimization time limit, all candidate matching degree combinations that have been searched are evaluated and sorted, and the candidate matching degree combination with the highest evaluation score is selected as the target matching degree combination.

10. The method for coordinated hydrogen thermal management of a solid-state hydrogen storage device and a fuel cell according to claim 9, characterized in that, The step of evaluating and ranking all candidate matching combinations searched, and selecting the candidate matching combination with the highest evaluation score as the target matching combination, includes: Iterate through all the candidate matching degree combinations that have been searched, calculate the evaluation score of each candidate matching degree combination, and select the candidate matching degree combination with the highest evaluation score as the target matching degree combination. The calculation of the evaluation score for each candidate matching degree combination includes: Extract the heat absorption matching degree and heat dissipation matching degree to be evaluated from the candidate matching degree combinations; determine the first dimension score based on the heat absorption matching degree to be evaluated and the heat absorption matching threshold, and determine the second dimension score based on the heat dissipation matching degree to be evaluated and the heat dissipation matching threshold. The evaluation score is obtained by weighted summation of the scores in the first dimension and the second dimension.

Images