A component matching method for fuel cell system based on advanced thermodynamic and energy quality coupling characteristics
By constructing a virtual system thermodynamic model and loss coupling matrix, and selecting the optimal combination of suitable models, the problems of energy grade depreciation and life cycle performance evolution among components in the fuel cell system are solved, and an efficient and economical system design is achieved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- CHONGQING UNIV
- Filing Date
- 2026-03-25
- Publication Date
- 2026-06-05
AI Technical Summary
Existing fuel cell system selection methods ignore energy quality depreciation losses between components and performance evolution over the entire life cycle, leading to over-design and lower-than-expected system efficiency. They also lack system-level coupling considerations and make it difficult to accurately distinguish between unavoidable and avoidable losses.
By constructing a virtual system thermodynamic model, unavoidable losses are identified. Using the loss coupling matrix and multi-objective optimization algorithm, the optimal matching model combination is selected. The loss evolution and cost throughout the entire life cycle are considered to achieve efficient matching between components.
It achieves global adaptability and long-term energy efficiency stability of fuel cell systems, eliminates over-design, reduces procurement costs, and enhances the competitiveness and robustness of the system.
Smart Images
Figure CN122153488A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of fuel cell system design and integration technology, and relates to a method for selecting and adapting fuel cell system components based on advanced energy and energy-mass coupling characteristics. Background Technology
[0002] With the transformation of the global energy structure, proton exchange membrane fuel cells (PEMFCs) have become a research hotspot in heavy-duty transportation and passenger vehicles due to their advantages such as zero emissions and high efficiency. A fuel cell system is a complex thermodynamic integration, comprising multiple subsystems including the stack, air supply system, hydrogen circulation system, and thermal management system. Its overall performance depends not only on the stack itself but also on the compatibility and suitability of various auxiliary components. In practical engineering design, the core technological bottleneck for achieving low-cost, high-reliability deployment lies in how to scientifically select and adapt components from a large hardware library to construct a system architecture that balances overall energy efficiency and lifespan stability.
[0003] Traditional system construction and selection criteria largely rely on semi-empirical performance envelope matching, i.e., energy balance analysis based on the first law of thermodynamics. This method rigidly focuses on the conservation of energy quantity, but ignores the depreciation and loss of energy quality due to irreversible processes. Even when traditional performance analysis techniques are introduced for evaluation, the limitations of its underlying logic are still exposed when guiding engineering decisions: Traditional selection methods often rely on a single dimension and lack system-level coupling considerations. They are based on simple matching of the nominal efficiency of each component under rated operating conditions, failing to fully account for the loss transfer caused by the mismatch of pressure, flow, and temperature characteristics between components. This often results in highly efficient individual components inducing excessive additional losses in other components after being integrated into the system, causing the overall system efficiency to fall short of expectations.
[0004] The lack of a quantitative trade-off between hardware performance redundancy and development costs makes it difficult for existing technologies to accurately distinguish which losses are unavoidable under technological limits and which are avoidable losses due to improper model specification matching. This leads designers to blindly adopt high-performance redundant components in pursuit of ultimate performance, resulting in serious over-design, significantly increasing system procurement costs and physical quality, and reducing product competitiveness.
[0005] Furthermore, the performance evolution over the entire life cycle is ignored. The energy efficiency of components decays non-linearly with operating time. Existing static selection methods cannot assess the energy quality stability of hardware combinations during long-term service, which can easily lead to accelerated fuel cell stack degradation in the later stages of the system's life due to component mismatch.
[0006] In summary, existing system selection and adaptation technologies have significant technical gaps in identifying improvement potential and tracing the source of mismatch effects. There is an urgent need to establish a deep thermodynamic framework capable of separating loss attributes and quantifying adaptation correlations. Summary of the Invention
[0007] In view of this, the purpose of the present invention is to provide a method for selecting and adapting fuel cell system components based on advanced energy and energy-mass coupling characteristics.
[0008] To achieve the above objectives, the present invention provides the following technical solution: A method for selecting and adapting components for a fuel cell system based on advanced energy and energy-mass coupling characteristics includes the following steps: S1: Construct a database containing multiple alternative components and establish a virtual system thermodynamic model with different hardware combinations; S2: Based on advanced slag removal methods, slag removal is performed on each candidate component combination to identify unavoidable slag caused by technical limits and to determine the performance saturation of candidate components in order to initially eliminate over-designed models. S3: By constructing a loss coupling matrix, the intensity of external loss generated by candidate components is quantitatively evaluated, and the optimal matching model combination is selected based on the criterion of minimizing the total avoidable loss of the system. S4: Taking into account the loss evolution and other costs throughout the entire life cycle, the optimal component selection combination of the system is finally determined.
[0009] Furthermore, step S1 specifically includes the following steps: S11: Standardized collection and characterization of performance data for multi-source heterogeneous components; S12: Establish a system-level virtual integrated thermodynamic model for adaptability evaluation; S13: Define the baseline operating condition spectrum and operating boundary conditions.
[0010] Furthermore, step S11 specifically includes the following steps: S111: Deconstruction and extraction of multi-dimensional characteristic parameters of components: collect and organize data of auxiliary components of fuel cell systems with different specifications and technical parameters; for active power consumption components, extract their flow characteristic curves and isentropic efficiency spectra under different speed and pressure gradients; for passive heat exchange components, extract their heat transfer coefficient and flow resistance characteristic data under different medium flow rates and temperature gradients. S112: Structured definition of component selection feature vector: Define a feature vector for each component model, including performance parameters, physical quality indicators and procurement cost indicators, to form a component candidate library that supports discrete calls; S113: Data normalization processing: Extract the nominal performance parameters of each component under steady state, perform dimensional normalization processing on test data from different sources, and establish a characteristic proxy model based on operating variables and component efficiency, power consumption and heat exchange capacity.
[0011] Furthermore, step S12 specifically includes the following steps: S121: Building a virtual system thermodynamic integrated architecture: Discretize and combine different models of components in the component candidate library to generate several virtual integrated system schemes with different hardware configuration characteristics, design the system output power requirements, and build a thermodynamic integrated architecture in the computing platform. S122: Calculation of energy flow and enthalpy at each node within the system: Based on the mass conservation, energy conservation, and momentum conservation equations at the interfaces of each component, the enthalpy, entropy, and enthalpy parameters of the reaction medium during the circulation process are calculated in real time. Enthalpy of the medium at the component interface and entropy Calculations were performed based on the medium composition and state, and the results were obtained from tables using Refprop software; the formulas for calculating the equilibrium equations of each component are as follows:
[0012] in, As alternative components The inlet medium As alternative components The export medium As alternative components The loss, As alternative components power, As alternative components The calories.
[0013] Furthermore, step S13 specifically includes the following steps: S131: Construct a working condition spectrum for typical application scenarios: Select and integrate power demand curves covering three typical scenarios: urban congestion, suburban cruising, and high-speed high load, to form standardized evaluation indicators for selection and testing cycles. S132: Unified Environmental Baseline Conditions and Reference Baselines: Defines the baseline environmental pressure during the selection process. The baseline ambient temperature serves as a unified reference state for calculating the values of each scheme. S133: Setting system operation constraints: Setting the boundary conditions for stack operation, including hydrogen-air pressure balance constraints, stack water-heat balance constraints, and power consumption limits for auxiliary components.
[0014] Furthermore, step S2 specifically includes the following steps: S21: Set the technical limit conditions for alternative components: For different functional components in the component alternative library, based on their physical properties and manufacturing process limits, preset the unavoidable operating parameter boundaries for each type of component. S22: Decoupling Calculation of Avoidable Losses for Alternative Components: Based on the pressure, temperature, flow rate, and chemical composition of each physical interface output from step S122, the energy-mass flow difference between the inlet and outlet of each component is calculated using the physical property database. The total loss of each alternative component under the full operating condition spectrum is then obtained. While keeping the system operating conditions unchanged, the performance parameters of the target candidate component are set to the technical limit values defined in step S21, and thermodynamic simulation calculations are performed again to obtain the unavoidable loss component of the component in the current integration environment. The calculation formula is:
[0015] in, For the analysis of alternative components under actual operating conditions The accumulated energy, Alternative components for the system under ideal operating conditions The ratio of energy dissipated to energy accumulated; By calculating the algebraic difference between the total loss and the unavoidable loss at the same time step, the avoidable loss with optimization potential is obtained. The calculation formula is:
[0016] S23: Evaluation model for the optimization potential and technical redundancy of alternative components: using the calculated... and Establish a saturation function to describe the improvement space for component selection. ,in For the unavoidable damage of this component Its total loss The ratio; for different load stages in the test condition spectrum, the saturation index Execute time-weighted or mileage-weighted integrals to obtain the average comprehensive performance saturation value of the candidate components throughout the entire selection and evaluation cycle; S24: Preliminary elimination of candidates based on advanced lossy disassembly results: by setting a first evaluation threshold to characterize the lower limit of hardware technology utilization. A second evaluation threshold characterizing the upper limit of the hardware energy quality optimization space. Establish a judgment model based on performance saturation, conduct design performance judgment, and finally output a sub-list of preferred components; If the performance saturation index of a certain alternative component Greater than the preset first threshold If the performance saturation index of a certain alternative component is within the technology saturation range, it is determined that the component is in the technology saturation range and is marked as an over-design scheme; Less than the preset second threshold If the component of that model has excessive avoidable damage, it will be marked as an ineffective solution.
[0017] Furthermore, step S3 specifically includes the following steps: S31: Construct a cross-component loss correlation matrix based on sensitivity analysis: For each alternative combination scheme in the preferred component sub-list output in step S24, keeping other system operating conditions constant, sequentially analyze the alternative components. The structural parameters or performance MAP points are subjected to step-by-step perturbation to obtain the target component. With components Loss response curve under changing characteristics; Sensitivity analysis was used to calculate the intervention weights for each component, and the weight values were then filled into the appropriate values. Dimensional correlation matrix In the context of the correlation matrix, the correlation strength is used to characterize the lossy coupling strength between components. Used to characterize the lossy coupling strength between each auxiliary component and the fuel cell stack in the system, as well as between the auxiliary components themselves; S32: Quantitative calculation of extrinsic damage avoidable by alternative components: using advanced component disassembly algorithms, combined with the aforementioned correlation matrix. Components in each group of alternative solutions The avoidable damage can be further broken down into endogenous avoidable damage. Exogenous factors can avoid damage and will Quantitatively allocate to each inducing source component to identify the externally responsible component that causes the energy efficiency degradation of the target component; Assuming the target component All other related components are in an ideal state. Calculate the target component in this state. Avoidable loss is defined as endogenous avoidable loss. ; Exogenous factors can avoid damage Represents other related components Hardware characteristic mismatch or operating point offset can induce target component The additional avoidable losses are calculated using the following formula:
[0018] S33: Define component selection compatibility evaluation factors The induced contribution and disturbance sensitivity of the calculated components are then weighted and fused with the component's own efficiency degradation rate to generate an adaptation factor. , as a quantitative indicator for measuring hardware performance matching; S34: Adaptive optimization decision based on minimizing total system loss: Based on the preferred component sub-list, calculate the total avoidable system loss for each combination scheme. Identify and lock the component combination that minimizes the overall loss of the system and determine it as the hardware solution with the highest adaptability; By topologically summing the intrinsic and extrinsic avoidable losses of all components within the combined scheme, a selection objective function is constructed with the goal of minimizing the total energy-mass degradation of the system. ,in The calculation formula is:
[0019] Automatically eliminate alternatives with low individual loss but high induced loss to ensure that the selection decision always leads to the overall optimum, and finally select the optimal solution from the sub-list of preferred options. The hardware combination with the minimum value is used to generate the optimal adaptation selection report.
[0020] Furthermore, step S4 specifically includes the following steps: S41: Prediction of performance degradation evolution throughout the entire life cycle: By introducing a time decay factor and cumulative operating conditions, the performance degradation process of each candidate component is simulated throughout its entire life cycle, and an advanced performance degradation model is used to calculate the avoidable performance degradation of each component under different operating durations. The growth slope is used to assess the energy quality stability of the candidate combination throughout its entire life cycle; The time decay factor is based on historical experimental data or semi-empirical models, and the critical components in the candidate pool decay over time. Energy efficiency change factor; Based on the decay factor and offline integration model, the total avoidable system loss for each alternative scheme is calculated over the entire lifespan. The changes over time; By differentiating the loss evolution curve, the slope of loss growth for each scheme over the entire life cycle is obtained, and the efficiency maintenance capability of the alternative combination during long-term operation is evaluated. S42: Constructing a multi-objective collaborative evaluation objective function: Based on a system comprehensive performance evaluation matrix with normalized weights, establish a multi-objective optimization evaluation model integrating energy efficiency, economy, and lightweighting indicators, and define the comprehensive scoring function for each scheme. as follows:
[0021] in, Life cycle energy efficiency factor, As a cost-benefit factor, The system's mass power density factor. , , The preset decision weight coefficients, and satisfy the following conditions: ; The lifecycle energy efficiency factor is the average system efficiency over the entire lifecycle, based on operating time. Overall system efficiency The calculation is as follows:
[0022] The cost-benefit factor is calculated by combining the procurement cost vector in the component candidate library to determine the amount of system-level avoidable loss reduction that can be achieved by a unit cost input. The calculation formula is as follows:
[0023] The mass power density factor is based on component feature vectors. The system power density index, calculated from the mass data, is given by the following formula:
[0024] S43: Determine the multi-criteria scheme evaluation and optimal solution: Use a multi-objective optimization algorithm to search and evaluate the alternative adaptation schemes output in step S42, and finally determine the optimal alternative scheme and generate a component adaptation list. Using multi-objective optimization algorithms to search for solutions in the solution space Given a set of constrained solutions, construct the optimal frontier surface; Based on perturbation operator Simulate cost or environmental fluctuations and calculate the scoring stability index. And identify in the optimal frontier that Maximize and robust metrics Alternative combinations that exceed a preset threshold are locked as the final adaptation solution.
[0025] Furthermore, the compatibility list includes the unique identifiers, supplier information, and corresponding physical parameter vectors of each subsystem component in the optimal solution, and provides the operating point benchmarks such as rated speed, rated pressure ratio, and rated temperature of the hardware combination under standard operating conditions, providing static reference initial values for downstream control algorithm design.
[0026] The beneficial effects of this invention are as follows: The fuel cell system loss identification and control method proposed in this invention, by integrating advanced loss analysis and multi-objective collaborative decision-making algorithms, achieves a technological leap from single-unit evaluation to system-level adaptation in the fuel cell system selection stage; by constructing a loss coupling matrix, it quantitatively evaluates the exogenous loss generated between components, thereby suppressing system energy quality degradation from the physical source and ensuring global adaptability; by using technical limits to identify unavoidable losses and introducing a performance saturation index to determine hardware technology utilization, it can eliminate "over-designed" models with excessive performance or unqualified solutions, which has significant value in lightweight design and cost reduction and efficiency improvement; by introducing a time decay factor to simulate the loss evolution over the entire life cycle, it can evaluate the long-term energy efficiency stability of candidate combinations, ensuring that the selection decision has high robustness under complex operating conditions; by comprehensively considering life cycle energy efficiency, economy, and mass power density, a multi-objective evaluation system is established, which improves R&D efficiency and provides static reference initial values for downstream control algorithm design.
[0027] Other advantages, objectives, and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination, or may be learned from practice of the invention. The objectives and other advantages of the invention can be realized and obtained through the following description. Attached Figure Description
[0028] To make the objectives, technical solutions, and advantages of the present invention clearer, the preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings, wherein: Figure 1 This is a flowchart illustrating the component adaptation and selection method for fuel cell systems based on advanced energy and energy-mass coupling characteristics as described in this invention. Detailed Implementation
[0029] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Unless otherwise specified, the following embodiments and features can be combined with each other.
[0030] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Therefore, the drawings only show the components related to the present invention and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.
[0031] In the following description, numerous details are explored to provide a more thorough explanation of embodiments of the invention. However, it will be apparent to those skilled in the art that embodiments of the invention may be practiced without these specific details. In other embodiments, well-known structures and devices are shown in block diagram form rather than in detail to avoid obscuring embodiments of the invention.
[0032] Example 1: This invention provides a method for selecting and adapting components for a fuel cell system based on advanced energy and energy-mass coupling characteristics, comprising the following steps: S1: Construct a database containing multiple alternative components and establish a virtual system thermodynamic model with different hardware combinations; Step S1 specifically includes the following steps: S11: Standardized collection and characterization of performance data for multi-source heterogeneous components; Step S11 specifically includes the following steps: S111: Deconstruction and extraction of multi-dimensional characteristic parameters of components; collection and organization of data on auxiliary components of fuel cell systems with different specifications and technical parameters; components include at least air compressors, hydrogen circulation devices, water pumps, and heat exchangers; for active power consumption components, extract their flow characteristic curves and isentropic efficiency maps under different speed and pressure gradients; for passive heat exchange components, extract their heat transfer coefficients and flow resistance characteristic data under different medium flow rates and temperature gradients. S112: Structured definition of component selection feature vectors; for each component model, a feature vector containing performance parameters, physical quality indicators, and procurement cost indicators is defined to form a component candidate library that supports discrete calls; S113: Data normalization processing; extract the nominal performance parameters of each component under steady state, perform dimensional normalization processing on test data from different sources, and establish a characteristic proxy model based on operating variables and component efficiency, power consumption and heat exchange capacity. S12: Establish a system-level "virtual integration" thermodynamic model for adaptability evaluation; Step S12 specifically includes the following steps: S121: Build a thermodynamic integrated architecture for a "virtual system"; Discretize and combine different models of components in the component candidate library to generate several virtual integrated system schemes with different hardware configuration characteristics, design the system output power requirements, and build a thermodynamic integrated architecture in the computing platform. S122: Calculation of energy flow and exergy flow of each node inside the system; Based on the mass conservation, energy conservation and momentum conservation equations at the interfaces of each component, calculate the enthalpy, entropy and exergy parameters of the reaction medium during the circulation process in real time; Enthalpy value of the medium at the component interface and entropy value Calculated respectively according to the medium composition and state, obtained by looking up the table with Refprop software, and the input code in Matlab is:
[0033]
[0034] The calculation formula for the exergy balance equation of each component is:
[0035] Among them, is the alternative component exergy (W) of the inlet medium, is the alternative component exergy (W) of the outlet medium, is the alternative component exergy loss (W), is the alternative component power (W), is the alternative component heat exergy (W); S13: Define the reference operating condition spectrum and operating boundary conditions; Step S13 specifically includes the following steps: S131: Construct the operating condition spectrum of typical application scenarios; Select and integrate the power demand curves covering three typical scenarios of urban congestion, suburban cruising and high-speed high load to form a standardized evaluation index for the selection test cycle; S132: Unify the environmental reference state and reference benchmark; Define the reference environmental pressure and reference environmental temperature during the selection process as the unified reference state for calculating the exergy value of each scheme; S133: Setting of system operation constraints; Set the operating boundary conditions of the fuel cell stack in the model, including hydrogen-air pressure balance constraint, fuel cell stack water-heat balance constraint and power consumption limit of auxiliary components S2: Based on the advanced exergy method, disassemble the exergy loss of each alternative component combination, identify the inevitable exergy loss caused by technical limits, and determine the performance saturation of the alternative components to initially eliminate over-designed models; Step S2 includes the following steps: S21: Set the technical limit conditions for alternative components; the technical limit refers to the highest efficiency state that the equipment or process can achieve under the current best feasible technical conditions, due to material properties, manufacturing processes, cost balance and safety limitations.
[0036] For different functional components in the component candidate library, based on their physical properties and manufacturing process limits, the unavoidable operating parameter boundaries for each type of component are preset. The unavoidable operating parameter boundaries include at least the limiting isentropic efficiency of the air compressor, the minimum heat transfer temperature difference of the heat exchanger, and the minimum energy loss rate of the motor drive, in order to characterize the loss bottom line that cannot be eliminated by optimization under current technical conditions. S22: Decoupling Calculation of Avoidable Losses for Alternative Components: Based on the pressure, temperature, flow rate, and chemical composition of each physical interface output from step S122, the energy-mass flow difference between the inlet and outlet of each component is calculated using the physical property database. The total loss of each alternative component under the full operating condition spectrum is then obtained. ; While keeping the system operating conditions unchanged, the performance parameters of the target candidate component are set to the technical limit values defined in step S21, and thermodynamic simulation calculations are performed again to obtain the unavoidable loss component of the component in the current integration environment. The calculation formula is:
[0037] in, For the analysis of alternative components under actual operating conditions The accumulated energy, Alternative components for the system under ideal operating conditions The ratio of energy dissipated to energy accumulated; By calculating the algebraic difference between total loss and unavoidable loss at the same time step, we can obtain the avoidable loss with optimization potential caused by improper component model and specification matching, design redundancy, or operating point deviation. The calculation formula is:
[0038] S23: Evaluation model for the optimization potential and technical redundancy of alternative components; using the calculated results and Establish a saturation function to describe the improvement space for component selection. ,in For the unavoidable damage of this component Its total loss The ratio; For different load stages in the test operating condition spectrum, the saturation index was analyzed. Execute time-weighted or mileage-weighted integrals to obtain the average comprehensive performance saturation value of the candidate components throughout the entire selection and evaluation cycle; S24: Initial elimination of candidates based on advanced lossy disassembly results; setting a first evaluation threshold to characterize the lower limit of hardware technology utilization. A second evaluation threshold characterizing the upper limit of the hardware energy quality optimization space. Establish a judgment model based on performance saturation, conduct design performance judgment, and finally output a sub-list of preferred components; If the performance saturation index of a certain alternative component Greater than the preset first threshold If so, the component model is determined to be in the technology saturation zone and is marked as an over-design scheme; If the performance saturation index of a certain alternative component Less than the preset second threshold If the component of that model has excessive avoidable damage, it will be marked as a substandard solution. S3: By constructing a loss coupling matrix, the intensity of external loss generated by candidate components is quantitatively evaluated, and the optimal matching model combination is selected based on the criterion of minimizing the total avoidable loss of the system; Step S3 includes the following steps: S31: Construct a cross-component loss correlation matrix based on sensitivity analysis; for each alternative combination scheme in the preferred component sub-list output in step S24, keeping other system operating conditions constant, sequentially process the alternative components. The structural parameters or performance MAP points are subjected to step-by-step perturbation to obtain the target component. With components Loss response curve under changing characteristics; Sensitivity analysis was used to calculate the intervention weights for each component, and the weight values were then filled into the appropriate values. Dimensional correlation matrix In this context, it is used to characterize the lossy coupling strength between components; Correlation Matrix Used to characterize the lossy coupling strength between each auxiliary component and the fuel cell stack in the system, as well as between the auxiliary components themselves; S32: The externality of alternative components can avoid quantitative calculation of damage; advanced damage disassembly algorithms are used, combined with the correlation matrix. Components in each group of alternative solutions The avoidable damage can be further broken down into endogenous avoidable damage. Exogenous factors can avoid damage and will Quantitatively allocate to each inducing source component to identify the externally responsible component that causes the energy efficiency degradation of the target component; Assuming the target component All other related components are in an ideal state. Calculate the target component in this state. Avoidable loss is defined as endogenous avoidable loss. The ideal state refers to a system in which there is no irreversibility. In this state, the process is completely reversible, that is, the loss of the entire system is zero. Exogenous factors can avoid damage Represents other related components Hardware characteristic mismatch or operating point offset can induce target component The additional avoidable losses are calculated using the following formula:
[0039] S33: Define component selection compatibility evaluation factors The induced contribution and disturbance sensitivity of the calculated components are then weighted and fused with the efficiency degradation rate of the components themselves to generate an adaptation factor. , as a quantitative indicator for measuring hardware performance matching; Induced contribution refers to the extent to which this component acts as an inducing source, causing other components in the system to generate unavoidable losses. The sum of these factors is used to assess the degree of their interference with the global energy and quality stability of the system. Disturbance sensitivity refers to the ability of a component, as an affected party, to avoid damage from external interference caused by other components in the system. The size of the sample is used to assess its robustness in complex integrated environments; S34: Adaptive optimization decision based on minimizing the total system loss; calculating the total avoidable system loss for each combination scheme based on the preferred component sub-list. Identify and lock the component combination that minimizes the overall loss of the system and determine it as the hardware solution with the highest adaptability; By topologically summing the intrinsic and extrinsic avoidable losses of all components within the combined scheme, a selection objective function is constructed with the goal of minimizing the total energy-mass degradation of the system. ,in The calculation formula is:
[0040] For alternative solutions that are "locally superior but overall inferior" (i.e., solutions with low individual losses but high induced losses), an automatic elimination command is executed to ensure that the selection decision always leads to the global optimum, and finally the optimal solution is selected from the optimal sublist. The hardware combination that yields the minimum value is used to generate an optimal adaptation selection report. S4: Taking into account the loss evolution throughout the entire life cycle and other costs, the optimal component selection combination for the system is finally determined; Step S44 includes the following steps: S41: Prediction of performance degradation evolution throughout the entire life cycle; by introducing time decay factors and cumulative operating conditions, the performance degradation process of each candidate component is simulated throughout its entire life cycle, and advanced performance degradation models are used to calculate the avoidable performance degradation of each component under different operating durations. The growth slope is used to assess the energy quality stability of the candidate combination throughout its entire life cycle; The time decay factor is based on historical experimental data or semi-empirical models, and refers to the decay of key components in the candidate pool (such as fuel cell catalyst, air compressor bearings, and heat exchanger flow channels) over operating time. Energy efficiency change factor; Based on the decay factor and offline integrated model, the total avoidable system loss for each alternative scheme is calculated over the entire lifespan (e.g., 5000h or 10000h). The changes over time; By differentiating the loss evolution curve, the slope of loss growth for each scheme over the entire life cycle is obtained, and the efficiency maintenance capability of the alternative combination during long-term operation is evaluated. S42: Construct a multi-objective collaborative evaluation objective function; establish a multi-objective optimization evaluation model integrating energy efficiency, economy, and lightweighting indicators based on a system comprehensive performance evaluation matrix with normalized weights, and define the comprehensive scoring function for each scheme. as follows:
[0041] in, Life cycle energy efficiency factor, As a cost-benefit factor, The system's mass power density factor. , , The preset decision weight coefficients, and satisfy the following conditions: ; Lifecycle efficiency factor is the average system efficiency over the entire lifecycle, based on operating time. Overall system efficiency The calculation is as follows:
[0042] The cost-benefit factor is calculated by combining the procurement cost vector in the component alternative library to determine the amount of system-level avoidable loss reduction that can be achieved by a unit cost input. The calculation formula is as follows:
[0043] The mass power density factor is based on component feature vectors. The system power density index, calculated from the mass data, is given by the following formula:
[0044] S43: Evaluation of multi-criteria schemes and determination of optimal solution; The alternative adaptation schemes output in step S42 are searched and evaluated using a multi-objective optimization algorithm to finally determine the optimal alternative scheme and generate a component adaptation list. Using multi-objective optimization algorithms to search for solutions in the solution space Given a set of constrained solutions, construct the optimal frontier surface; Based on perturbation operator Simulate cost or environmental fluctuations and calculate the scoring stability index. And identify in the optimal frontier that Maximize and robust metrics Alternative combinations that exceed a preset threshold are locked as the final adaptation solution; The compatibility list includes the unique identifiers, supplier information, and corresponding physical parameter vectors of each subsystem component in the optimal solution. It also provides the operating point benchmarks for the hardware combination under standard operating conditions, such as rated speed, rated pressure ratio, and rated temperature, providing static reference initial values for downstream control algorithm design.
[0045] Example 2: An electronic device, comprising a memory and a processor; The memory is used to store computer programs; The processor is configured to implement the method described in Embodiment 1 when executing the computer program.
[0046] Example 3: A computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described in Embodiment 1.
[0047] Example 4: A computer program product includes a computer program that, when executed by a processor, implements the method described in Example 1.
[0048] In the above embodiments, the reference to "this embodiment" in the specification indicates that a specific feature, structure, or characteristic described in connection with the embodiment is included in at least some embodiments, but not necessarily all embodiments. Multiple appearances of "this embodiment" do not necessarily refer to the same embodiment.
[0049] In the above embodiments, although the invention has been described in conjunction with specific embodiments thereof, many substitutions, modifications, and variations of these embodiments will be apparent to those skilled in the art from the foregoing description. For example, other memory structures (e.g., dynamic RAM (DRAM)) may be used with the embodiments discussed. The embodiments of the invention are intended to cover all such substitutions, modifications, and variations falling within the broad scope of the appended claims.
[0050] As will be understood by those skilled in the art, the computer-readable storage medium described in this embodiment allows for the implementation of all or part of the steps in the above method embodiments by computer program-related hardware. The aforementioned computer program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.
[0051] The electronic terminal provided in this embodiment includes a processor, a memory, a transceiver, and a communication interface. The memory and the communication interface are connected to the processor and the transceiver and complete communication between them. The memory is used to store computer programs, the communication interface is used to perform communication, and the processor and the transceiver are used to run the computer programs, so that the electronic terminal performs the steps of the above method.
[0052] In this embodiment, the memory may include random access memory (RAM) and may also include non-volatile memory, such as at least one disk storage device.
[0053] The processors mentioned above can be general-purpose processors, including central processing units (CPUs), network processors (NPs), etc.; they can also be digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.
[0054] This invention can be used in a wide range of general-purpose or special-purpose computing system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices, etc.
[0055] This invention can be described in the general context of computer-executable instructions, such as program modules, that are executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform a specific task or implement a specific abstract data type. This invention can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.
[0056] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.
Claims
1. A method for selecting and adapting components for a fuel cell system based on advanced energy and energy-mass coupling characteristics, characterized in that: Includes the following steps: S1: Construct a database containing multiple alternative components and establish a virtual system thermodynamic model with different hardware combinations; S2: Based on advanced slag removal methods, slag removal is performed on each candidate component combination to identify unavoidable slag caused by technical limits and to determine the performance saturation of candidate components in order to initially eliminate over-designed models. S3: By constructing a loss coupling matrix, the intensity of external loss generated by candidate components is quantitatively evaluated, and the optimal matching model combination is selected based on the criterion of minimizing the total avoidable loss of the system. S4: Taking into account the loss evolution and other costs throughout the entire life cycle, the optimal component selection combination of the system is finally determined.
2. The fuel cell system component adaptation and selection method based on advanced energy and energy-mass coupling characteristics according to claim 1, characterized in that: Step S1 specifically includes the following steps: S11: Standardized collection and characterization of performance data for multi-source heterogeneous components; S12: Establish a system-level virtual integrated thermodynamic model for adaptability evaluation; S13: Define the baseline operating condition spectrum and operating boundary conditions.
3. The fuel cell system component adaptation and selection method based on advanced energy and energy-mass coupling characteristics as described in claim 2, characterized in that: Step S11 specifically includes the following steps: S111: Deconstruction and extraction of multi-dimensional characteristic parameters of components: collect and organize data of auxiliary components of fuel cell systems with different specifications and technical parameters; for active power consumption components, extract their flow characteristic curves and isentropic efficiency spectra under different speed and pressure gradients; for passive heat exchange components, extract their heat transfer coefficient and flow resistance characteristic data under different medium flow rates and temperature gradients. S112: Structured definition of component selection feature vector: Define a feature vector for each component model, including performance parameters, physical quality indicators and procurement cost indicators, to form a component candidate library that supports discrete calls; S113: Data normalization processing: Extract the nominal performance parameters of each component under steady state, perform dimensional normalization processing on test data from different sources, and establish a characteristic proxy model based on operating variables and component efficiency, power consumption and heat exchange capacity.
4. The fuel cell system component adaptation and selection method based on advanced energy and energy-mass coupling characteristics according to claim 2, characterized in that: Step S12 specifically includes the following steps: S121: Building a virtual system thermodynamic integrated architecture: Discretize and combine different models of components in the component candidate library to generate several virtual integrated system schemes with different hardware configuration characteristics, design the system output power requirements, and build a thermodynamic integrated architecture in the computing platform. S122: Calculation of energy flow and enthalpy at each node within the system: Based on the mass conservation, energy conservation, and momentum conservation equations at the interfaces of each component, the enthalpy, entropy, and enthalpy parameters of the reaction medium during the circulation process are calculated in real time. Enthalpy of the medium at the component interface and entropy Calculations were performed based on the medium composition and state, and the results were obtained from tables using Refprop software; the formulas for calculating the equilibrium equations of each component are as follows: in, As alternative components The inlet medium As alternative components The export medium As alternative components The loss, As alternative components power, As alternative components The calories.
5. The fuel cell system component adaptation and selection method based on advanced energy and energy-mass coupling characteristics according to claim 2, characterized in that: Step S13 specifically includes the following steps: S131: Construct a working condition spectrum for typical application scenarios: Select and integrate power demand curves covering three typical scenarios: urban congestion, suburban cruising, and high-speed high load, to form standardized evaluation indicators for selection and testing cycles. S132: Unified Environmental Baseline Conditions and Reference Baselines: Defines the baseline environmental pressure during the selection process. The baseline ambient temperature serves as a unified reference state for calculating the values of each scheme. S133: Setting system operation constraints: Setting the boundary conditions for stack operation, including hydrogen-air pressure balance constraints, stack water-heat balance constraints, and power consumption limits for auxiliary components.
6. The fuel cell system component adaptation and selection method based on advanced energy and energy-mass coupling characteristics according to claim 4, characterized in that: Step S2 specifically includes the following steps: S21: Set the technical limit conditions for alternative components: For different functional components in the component alternative library, based on their physical properties and manufacturing process limits, preset the unavoidable operating parameter boundaries for each type of component. S22: Decoupling Calculation of Avoidable Losses for Alternative Components: Based on the pressure, temperature, flow rate, and chemical composition of each physical interface output from step S122, the energy-mass flow difference between the inlet and outlet of each component is calculated using the physical property database. The total loss of each alternative component under the full operating condition spectrum is then obtained. While keeping the system operating conditions unchanged, the performance parameters of the target candidate component are set to the technical limit values defined in step S21, and thermodynamic simulation calculations are performed again to obtain the unavoidable loss component of the component in the current integration environment. The calculation formula is: in, For the analysis of alternative components under actual operating conditions The accumulated energy, Alternative components for the system under ideal operating conditions The ratio of energy dissipated to energy accumulated; By calculating the algebraic difference between the total loss and the unavoidable loss at the same time step, the avoidable loss with optimization potential is obtained. The calculation formula is: S23: Evaluation model for the optimization potential and technical redundancy of alternative components: using the calculated... and Establish a saturation function to describe the improvement space for component selection. ,in For the unavoidable damage of this component Its total loss The ratio; for different load stages in the test condition spectrum, the saturation index Execute time-weighted or mileage-weighted integrals to obtain the average comprehensive performance saturation value of the candidate components throughout the entire selection and evaluation cycle; S24: Preliminary elimination of candidates based on advanced lossy disassembly results: by setting a first evaluation threshold to characterize the lower limit of hardware technology utilization. A second evaluation threshold characterizing the upper limit of the hardware energy quality optimization space. Establish a judgment model based on performance saturation, conduct design performance judgment, and finally output a sub-list of preferred components; If the performance saturation index of a certain alternative component Greater than the preset first threshold If the performance saturation index of a certain alternative component is within the technology saturation range, it is determined that the component is in the technology saturation range and is marked as an over-design scheme; Less than the preset second threshold If the component of that model has excessive avoidable damage, it will be marked as an ineffective solution.
7. The fuel cell system component adaptation and selection method based on advanced energy and energy-mass coupling characteristics as described in claim 6, characterized in that: Step S3 specifically includes the following steps: S31: Construct a cross-component loss correlation matrix based on sensitivity analysis: For each alternative combination scheme in the preferred component sub-list output in step S24, keeping other system operating conditions constant, sequentially analyze the alternative components. The structural parameters or performance MAP points are subjected to step-by-step perturbation to obtain the target component. With components Loss response curve under changing characteristics; Sensitivity analysis was used to calculate the intervention weights for each component, and the weight values were then filled into the appropriate values. Dimensional correlation matrix In the context of the correlation matrix, the correlation strength is used to characterize the lossy coupling strength between components. Used to characterize the lossy coupling strength between each auxiliary component and the fuel cell stack in the system, as well as between the auxiliary components themselves; S32: Quantitative calculation of extrinsic damage avoidable by alternative components: using advanced component disassembly algorithms, combined with the aforementioned correlation matrix. Components in each group of alternative solutions The avoidable damage can be further broken down into endogenous avoidable damage. Exogenous factors can avoid damage and will Quantitatively allocate to each inducing source component to identify the externally responsible component that causes the energy efficiency degradation of the target component; Assuming the target component All other related components are in an ideal state. Calculate the target component in this state. Avoidable loss is defined as endogenous avoidable loss. ; Exogenous factors can avoid damage Represents other related components Hardware characteristic mismatch or operating point offset can induce target component The additional avoidable losses are calculated using the following formula: S33: Define component selection compatibility evaluation factors The induced contribution and disturbance sensitivity of the calculated components are then weighted and fused with the component's own efficiency degradation rate to generate an adaptation factor. , as a quantitative indicator for measuring hardware performance matching; S34: Adaptive optimization decision based on minimizing total system loss: Based on the preferred component sub-list, calculate the total avoidable system loss for each combination scheme. Identify and lock the component combination that minimizes the overall loss of the system and determine it as the hardware solution with the highest adaptability; By topologically summing the intrinsic and extrinsic avoidable losses of all components within the combined scheme, a selection objective function is constructed with the goal of minimizing the total energy-mass degradation of the system. ,in The calculation formula is: Automatically eliminate alternatives with low individual loss but high induced loss to ensure that the selection decision always leads to the overall optimum, and finally select the optimal solution from the sub-list of preferred options. The hardware combination with the minimum value is used to generate the optimal adaptation selection report.
8. The method for adapting and selecting components of a fuel cell system based on advanced energy and energy-mass coupling characteristics according to claim 1, characterized in that: Step S4 Specifically, the following steps are included: S41: Prediction of performance degradation evolution throughout the entire life cycle: By introducing a time decay factor and cumulative operating conditions, the performance degradation process of each candidate component is simulated throughout its entire life cycle, and an advanced performance degradation model is used to calculate the avoidable performance degradation of each component under different operating durations. The growth slope is used to assess the energy quality stability of the candidate combination throughout its entire life cycle; The time decay factor is based on historical experimental data or semi-empirical models, and the critical components in the candidate pool decay over time. Energy efficiency change factor; Based on the decay factor and offline integration model, the total avoidable system loss for each alternative scheme is calculated over the entire lifespan. The changes over time; By differentiating the loss evolution curve, the slope of loss growth for each scheme over the entire life cycle is obtained, and the efficiency maintenance capability of the alternative combination during long-term operation is evaluated. S42: Constructing a multi-objective collaborative evaluation objective function: Based on a system comprehensive performance evaluation matrix with normalized weights, establish a multi-objective optimization evaluation model integrating energy efficiency, economy, and lightweighting indicators, and define the comprehensive scoring function for each scheme. as follows: in, Life cycle energy efficiency factor, As a cost-benefit factor, The system's mass power density factor. , , The preset decision weight coefficients, and satisfy the following conditions: ; The lifecycle energy efficiency factor is the average system efficiency over the entire lifecycle, based on operating time. Overall system efficiency The calculation is as follows: The cost-benefit factor is calculated by combining the procurement cost vector in the component candidate library to determine the amount of system-level avoidable loss reduction that can be achieved by a unit cost input. The calculation formula is as follows: The mass power density factor is based on component feature vectors. The system power density index, calculated from the mass data, is given by the following formula: S43: Determine the multi-criteria scheme evaluation and optimal solution: Use a multi-objective optimization algorithm to search and evaluate the alternative adaptation schemes output in step S42, and finally determine the optimal alternative scheme and generate a component adaptation list. Using multi-objective optimization algorithms to search for solutions in the solution space Given a set of constrained solutions, construct the optimal frontier surface; Based on perturbation operator Simulate cost or environmental fluctuations and calculate the scoring stability index. And identify in the optimal frontier that Maximize and robust metrics Alternative combinations that exceed a preset threshold are locked as the final adaptation solution.
9. The fuel cell system component adaptation and selection method based on advanced energy and energy-mass coupling characteristics as described in claim 8, characterized in that: The compatibility list includes the unique identifiers, supplier information, and corresponding physical parameter vectors of each subsystem component in the optimal solution. It also provides the operating point benchmarks for the hardware combination under standard operating conditions, such as rated speed, rated pressure ratio, and rated temperature, providing static reference initial values for downstream control algorithm design.