Layered power distribution method and system for double-stack fuel cells

Abstract

The invention belongs to the technical field of power distribution methods, and particularly relates to a layered power distribution method and system for a double-stack fuel cell, and the method comprises the steps: constructing a layered control framework of bottom-layer double-stack power distribution and top-layer hydrogen power system / supercapacitor power distribution; defining a double-stack joint efficiency function and a super-capacitor energy cost coefficient; calculating a double-reactor optimal power point based on power consumption parameter fusion; calculating the optimal output power of the hydrogen power system based on a cost power consumption algorithm; the bottom-layer controller outputs the optimal power of the double reactors to the actuator, and the top-layer controller outputs a power distribution instruction of the hydrogen power system and the super capacitor. The system comprises a corresponding control module and an execution module. According to the invention, the problem of performance difference coordination of the double-stack fuel cell is solved, accurate control of power distribution is realized, system efficiency and dynamic stability are improved, and hydrogen consumption and power fluctuation are reduced.

Description

Technical Field

[0001] This invention belongs to the technical field of power distribution methods, specifically relating to a hierarchical power distribution method and system for dual-stack fuel cells. Background Technology

[0002] With the promotion and application of hydrogen energy technology in the transportation sector, dual-stack fuel cell hybrid power systems have become the mainstream power solution for medium-capacity rubber-tired buses and other scenarios. Currently, power distribution is usually achieved using state machine control, particle swarm optimization algorithms, or multi-stack fuel cell collaborative strategies. Among these, the equivalent hydrogen consumption algorithm is a common optimization method, aiming to improve system efficiency through power management. Supercapacitors, as auxiliary energy units, undertake peak power compensation and braking energy recovery functions, complementing the fuel cells.

[0003] However, current power distribution methods cannot effectively coordinate the performance differences of dual-stack fuel cells under different life states, resulting in low overall system efficiency, dynamic response delay and poor stability. This problem leads to insufficient utilization of high-efficiency stacks and excessive load on inefficient stacks, which exacerbates energy consumption and equipment wear. Summary of the Invention

[0004] The technical problem to be solved by this invention is to overcome the problem that traditional power distribution methods cannot effectively coordinate the performance differences between dual-stack fuel cells, resulting in low system efficiency, poor dynamic response, and insufficient stability.

[0005] To address the aforementioned technical problems, a first aspect of the present invention discloses a method for hierarchical power allocation in a dual-stack fuel cell, the method comprising: A hierarchical control framework is constructed, which includes a bottom-level dual-reactor power distribution controller and a top-level hydrogen power system / supercapacitor power distribution controller. The bottom-level controller aims to maximize the joint efficiency of the two reactors, while the top-level controller aims to minimize the total power consumption of the system. Define a dual-stack joint efficiency function and establish a mathematical model based on the actual power consumption characteristics of fuel cells, where the power consumption fitting coefficient of fuel cell 1 is a specific value and the power consumption fitting coefficient of fuel cell 2 is a specific value. Define the supercapacitor energy cost coefficient and perform iterative calculations based on the energy state, where the energy state is calculated through voltage extrema. Calculate the optimal power point of the dual-stack system and obtain a closed-form solution by taking the partial derivative of the joint efficiency function. The optimal output power of the hydrogen power system is calculated by combining the fusion power consumption function and the supercapacitor internal resistance model. The underlying controller outputs the optimal power of the dual stacks to the actuators according to the control cycle; The top-level controller synchronously outputs power distribution commands between the hydrogen power system and the supercapacitor.

[0006] As an optional implementation, in the first aspect of the invention, the construction of a hierarchical control framework comprising a bottom-level dual-reactor power distribution controller and a top-level hydrogen power system / supercapacitor power distribution controller includes: The underlying controller is configured to handle the coordinated power distribution between the two fuel cells, using a specific control cycle; A top-level controller is set up to handle the power matching between the hydrogen power system and the supercapacitor, so as to achieve dynamic power balance. Establish real-time data communication between the bottom and top level controllers, and achieve data synchronization through the DSP processor.

[0007] As an optional implementation, in the first aspect of the present invention, defining the dual-stack joint efficiency function specifically includes: A joint efficiency calculation model is established based on actual test data of dual-stack fuel cells, where the power consumption fitting coefficient of fuel cell 1 is a specific combination of values, and the power consumption fitting coefficient of fuel cell 2 is a specific combination of values. The definition of the supercapacitor energy cost coefficient specifically includes: calculating the energy state based on the measured voltage value of the supercapacitor and the preset voltage extreme value, and iteratively updating it in combination with the module capacity parameters.

[0008] As an optional implementation, in the first aspect of the present invention, the calculation of the optimal power point of the dual-stack assembly specifically includes: The optimal power distribution ratio between fuel cell 1 and fuel cell 2 is obtained by taking the partial derivative of the joint efficiency function. The fusion power consumption function is constructed to express the power consumption characteristics of the dual-stack system in a unified manner, where the fusion coefficient is calculated by a specific formula.

[0009] As an optional implementation, in the first aspect of the present invention, calculating the optimal output power of the hydrogen power system specifically includes: Establish a total power consumption function that includes the dual-stack fusion power consumption function and the supercapacitor internal resistance model; The optimal output point is determined based on the DC bus power demand, supercapacitor terminal voltage, and internal resistance. Calculate the supercapacitor compensation power to achieve dynamic power matching.

[0010] As an optional implementation, in the first aspect of the present invention, the underlying controller outputting the optimal dual-stack power to the actuator during a control cycle specifically includes: The optimal power command for fuel cell 1 and fuel cell 2 is output according to the set control cycle; Real-time acquisition of dual-reactor operating status parameters, including output power and system efficiency, and dynamic adjustment of power allocation strategy.

[0011] As an optional implementation, in the first aspect of the present invention, the top-level controller synchronously outputs power distribution instructions between the hydrogen power system and the supercapacitor, specifically including: Simultaneously output the optimal power value of the hydrogen power system and the supercapacitor compensation power value; The power output limit range is set according to the extreme value of the supercapacitor voltage, and overvoltage protection is implemented.

[0012] As an optional implementation, the first aspect of the present invention further includes a system parameter configuration step: Set the supercapacitor module capacity to a specific value, the internal resistance to a specific value, and the voltage operating range to a specific range; Set the control cycle to a specific duration to ensure the system's real-time response performance.

[0013] A second aspect of this invention discloses a hierarchical power allocation system for a dual-stack fuel cell, used to implement the hierarchical power allocation method for a dual-stack fuel cell described in any of the above embodiments, the system comprising: The underlying control module is configured to perform dual-stack power allocation calculations and joint efficiency function solving. The top-level control module is configured to handle power matching between the hydrogen power system and the supercapacitor, as well as energy cost factor updates. The data communication module is used to realize real-time data exchange and command synchronization between controllers; The actuator drive module is used to receive control commands and drive the fuel cell and supercapacitor units to work together.

[0014] A third aspect of the present invention discloses another tiered power distribution system for dual-stack fuel cells, the system comprising: Memory containing executable program code; A processor coupled to the memory; The processor calls the executable program code stored in the memory to execute a hierarchical power allocation method for a dual-stack fuel cell disclosed in the first aspect of the present invention.

[0015] The fourth aspect of the present invention discloses a computer-readable storage medium storing computer instructions, which, when invoked by a processor, are used to execute a hierarchical power allocation method for a dual-stack fuel cell disclosed in the first aspect of the present invention.

[0016] Compared with the prior art, the beneficial effects of the present invention are: 1. By constructing a hierarchical framework of bottom-level dual-reactor collaboration and top-level hydrogen power system / supercapacitor matching, a complete mathematical modeling system was established, which enabled the power allocation algorithm to have a clear closed-form solution, solving the problem that traditional methods are difficult to deploy quickly in actual control systems.

[0017] 2. Based on the dynamic calculation of the joint efficiency function of the two reactors and the real-time iterative update of the supercapacitor energy cost coefficient, the performance difference between the two reactors is accurately quantified, overcoming the problem of insufficient accuracy caused by the fixed parameters of the traditional equivalent hydrogen consumption algorithm.

[0018] 3. By using a joint efficiency function to model the power consumption characteristics of the fuel cell stack under different life states, the higher-performance fuel cell can obtain a reasonable power allocation ratio, while avoiding excessive load on the inefficient fuel cell stack, thus achieving collaborative optimization of dual-stack operation.

[0019] 4. Relying on the hierarchical control mechanism and supercapacitor voltage constraint management, a complete power distribution closed-loop adjustment system has been established, which effectively smooths out power fluctuations under dynamic operating conditions. Attached Figure Description

[0020] The specific embodiments of the present invention will be further described in detail below with reference to the accompanying drawings, wherein: Figure 1 This is a schematic flowchart of a hierarchical power allocation method for a dual-stack fuel cell disclosed in an embodiment of the present invention; Figure 2 This is a schematic diagram of a dual-stack fuel cell hierarchical power distribution system disclosed in an embodiment of the present invention; Figure 3 This is a schematic diagram of another dual-stack fuel cell hierarchical power distribution system disclosed in an embodiment of the present invention; Figure 4 The figure shows the fitting curve of polarization characteristics and output power of the fuel cell system disclosed in the embodiments of the present invention. Figure 5 This is a schematic diagram of the online control process disclosed in an embodiment of the present invention; Figure 6 This is a schematic diagram of the dual-stack hybrid power system topology disclosed in an embodiment of the present invention; Figure 7 This is a schematic diagram of the efficiency distribution of a hydrogen power system disclosed in an embodiment of the present invention. Detailed Implementation

[0021] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. 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.

[0022] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this invention are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, apparatus, product, or end that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or ends.

[0023] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0024] This invention discloses a hierarchical power allocation method and system for dual-stack fuel cells. The method first performs intelligent routing judgment on user requests, directing simple and high-frequency requests to efficient vector retrieval paths. Then, it introduces a confidence threshold that dynamically adjusts with the system load to ensure that the computationally intensive but more powerful sparse expert model path is invoked only when the simple path result is unreliable. Thus, while ensuring the quality of response, it achieves on-demand allocation of computing resources and maximizes the overall efficiency of the system, effectively solving the core problems of high cost, high latency, and insufficient high-concurrency processing capabilities of current intelligent customer service systems.

[0025] Example 1 Please see Figure 1 , Figure 1 This is a schematic flowchart of a hierarchical power allocation method for a dual-stack fuel cell disclosed in an embodiment of the present invention. Figure 1 The described hierarchical power allocation method for dual-stack fuel cells is applied to a data processing chip, processing terminal, or processing server, and the processing server can be a local server or a cloud server; this embodiment of the invention does not limit the application. Figure 1 As shown, the tiered power allocation method for dual-stack fuel cells may include the following operations: 101. Construct a hierarchical control framework that includes a bottom-level dual-reactor power distribution controller and a top-level hydrogen power system / supercapacitor power distribution controller. The bottom-level controller aims to maximize the combined efficiency of the two reactors, while the top-level controller aims to minimize the total power consumption of the system.

[0026] Specifically, the hierarchical control framework focuses on maximizing the joint efficiency of the dual-stack fuel cell through the bottom-level controller, achieving rapid solution and real-time control of the power allocation algorithm, thus improving the system's practicality and response speed. The design of this invention enables the control strategy to have a definite closed-form solution, effectively adapting to the needs of dynamic operating conditions.

[0027] It is evident that the top-level controller, with the goal of minimizing the total power consumption of the system, optimizes the power matching between the hydrogen power system and the supercapacitor, improves the allocation accuracy and energy efficiency, and enhances the overall operational stability.

[0028] 102. Define the dual-stack joint efficiency function and establish a mathematical model based on the actual power consumption characteristics of fuel cells.

[0029] Specifically, by defining a joint efficiency function for the two fuel cells, a unified efficiency optimization model is established by mathematically correlating the power consumption characteristics of fuel cells with different performance levels. This allows the output power of the two fuel cells to be adjusted collaboratively, thereby improving the overall system efficiency. The functional expression method of this invention overcomes the problem of insufficient coordination of fuel cell performance differences in traditional methods, and achieves deep integration of power consumption characteristics.

[0030] As can be seen, establishing a mathematical model based on the actual power consumption characteristics of fuel cells and using coefficients fitted with measured data accurately reflects the energy consumption patterns of the fuel cell stack under different lifespan states, providing a precise computational basis for power allocation. This model supports closed-form solution solving, enhancing the practicality and real-time performance of the algorithm and optimizing control performance under dynamic operating conditions.

[0031] 103. Define the supercapacitor energy cost coefficient and perform iterative calculations based on the energy state, where the energy state is calculated through voltage extrema.

[0032] Specifically, the supercapacitor energy cost coefficient is defined and iteratively calculated based on the energy state, enabling dynamic tracking of the supercapacitor's charging and discharging process and improving the real-time performance and adaptability of power allocation. The iterative mechanism of this invention continuously optimizes the accuracy of energy state assessment and enhances the system's responsiveness to changes in operating conditions.

[0033] As can be seen, calculating the energy state through voltage extrema ensures the reliability of energy storage level quantification, providing a technical foundation for the accurate quantification of hydrogen energy costs. This method optimizes the energy consumption efficiency of power matching by directly correlating voltage parameters with the actual energy storage state.

[0034] 104. Calculate the optimal power point of the two stacks and obtain a closed-form solution by taking the partial derivative of the joint efficiency function.

[0035] Specifically, by taking the partial derivative of the joint efficiency function to obtain a closed-form solution, efficient calculation of the optimal power point for the dual-stack power distribution is achieved, improving the real-time performance and response speed of the control algorithm. This method avoids the complex iterative process of traditional optimization algorithms, enabling power allocation to quickly adapt to dynamic operating conditions.

[0036] As can be seen, the application of the closed-form solution ensures the accuracy and reliability of power distribution calculation, optimizes the cooperative output efficiency of the dual-stack fuel cell, enhances the stability of system operation, and provides a more accurate power reference for the power system.

[0037] 105. Calculate the optimal output power of the hydrogen power system by combining the fusion power consumption function and the supercapacitor internal resistance model.

[0038] Specifically, by combining the fusion power consumption function with the supercapacitor internal resistance model to solve for the optimal output power of the hydrogen power system, the total power consumption of the system is accurately optimized, improving the overall efficiency of energy management. This invention comprehensively considers the power consumption characteristics of the dual-stack and the internal resistance loss of the capacitor, providing a more comprehensive calculation basis for power allocation.

[0039] It is evident that this integrated solution method enhances the coordination of power allocation and optimizes the collaborative performance of the hydrogen power system and the supercapacitor. By establishing a complete power consumption model, the system's adaptability and operational stability under dynamic conditions are improved.

[0040] 106. The bottom-level controller outputs the optimal power of the dual stacks to the actuators according to the control cycle.

[0041] Specifically, the underlying controller outputs the optimal power of the dual stacks to the actuators according to the set control cycle, ensuring the real-time and synchronous nature of the power distribution command and improving the system's responsiveness to dynamic operating conditions. This periodic output mechanism of the present invention enables power adjustment to keep up with changes in system state in a timely manner, thereby enhancing the timeliness of control.

[0042] As can be seen, by precisely managing the control cycle, stable power output of the dual-stack system is achieved, the working state of the fuel cell is optimized, the present invention ensures the continuity and reliability of power commands, and improves the smoothness of the power system operation.

[0043] 107. The top-level controller synchronously outputs power distribution commands between the hydrogen power system and the supercapacitor.

[0044] Specifically, the top-level controller synchronously outputs power allocation commands for the hydrogen power system and the supercapacitor, realizing dynamic power matching between the two energy units and improving the coordination of system energy management.

[0045] It is evident that by synchronously outputting control commands, timing deviations during power distribution are effectively avoided, thereby enhancing the stability and reliability of the system operation.

[0046] As an optional embodiment, in the steps described, constructing a hierarchical control framework comprising a bottom-level dual-reactor power distribution controller and a top-level hydrogen power system / supercapacitor power distribution controller includes: The underlying controller is configured to handle the coordinated power distribution between the two fuel cells, using a specific control cycle; A top-level controller is set up to handle the power matching between the hydrogen power system and the supercapacitor, so as to achieve dynamic power balance. Establish real-time data communication between the bottom and top level controllers, and achieve data synchronization through the DSP processor.

[0047] In this embodiment of the invention, the specific implementation of constructing the hierarchical control framework includes: setting up a bottom-level controller specifically for handling power coordination and allocation between the two fuel cells; this controller performs calculations using a fixed control cycle of no more than 1 millisecond, and calculates power allocation based on a joint efficiency function by real-time acquisition of the net output power parameters of the two-stack system. Setting up a top-level controller specifically for handling power matching between the hydrogen power system and the supercapacitor; this controller calculates the optimal output power in real-time using a cost-power algorithm and dynamically adjusts the power allocation ratio according to the DC bus power demand to achieve dynamic power balance. Establishing a real-time data communication channel between the bottom and top-level controllers; using a digital signal processor as the core processing unit; and achieving millisecond-level synchronous transmission of control commands and status parameters through a parallel data bus to ensure real-time consistency and coordination of data between the two controllers.

[0048] As can be seen, by setting up a hierarchical control architecture and establishing a real-time data communication mechanism, the power allocation task can be effectively decomposed and collaboratively processed. This design allows the lower-level controller to focus on dual-stacking power optimization, while the upper-level controller is responsible for system-level power matching. Through the rapid data synchronization of the DSP processor, the real-time response capability and operational reliability of the control system are ensured.

[0049] As an optional embodiment, in the above steps, defining the dual-stack joint efficiency function specifically includes: A joint efficiency calculation model is established based on actual test data of dual-stack fuel cells, where the power consumption fitting coefficient of fuel cell 1 is a specific combination of values, and the power consumption fitting coefficient of fuel cell 2 is a specific combination of values. The definition of the supercapacitor energy cost coefficient specifically includes: calculating the energy state based on the measured voltage value of the supercapacitor and the preset voltage extreme value, and iteratively updating it in combination with the module capacity parameters.

[0050] In this embodiment of the invention, the specific implementation of defining the joint efficiency function of the dual-stack fuel cells includes: establishing a joint efficiency calculation model based on actual power consumption test data of the dual-stack fuel cells. This model expresses the power consumption characteristics in the form of a quadratic function, where the power consumption fitting coefficients for fuel cell 1 are a1 = 2.9 x 10^-3, b1 = 1.546, and c1 = 7.0958; and the power consumption fitting coefficients for fuel cell 2 are a2 = 3.6 x 10^-3, b2 = 1.7231, and c2 = 6.6348. The joint efficiency function is calculated by the ratio of the net output power of the dual-stack system to the sum of the power consumption of each fuel cell, accurately reflecting the performance differences of the stacks at different life stages.

[0051] The specific implementation method for defining the supercapacitor energy cost coefficient includes: calculating the energy state based on real-time collected supercapacitor terminal voltage measurements and preset voltage extreme values, where the minimum voltage is 540 volts and the maximum voltage is 720 volts. The energy state is quantified by the ratio of the voltage squared difference to the extreme value squared difference. Combined with the supercapacitor module capacity parameter of 363 farads, an iterative algorithm is used to update the energy cost coefficient. Each iteration is based on a weighted calculation of historical energy states and current actual power consumption, achieving dynamic adjustment of the coefficient.

[0052] As can be seen, by establishing a joint efficiency function for the two stacks based on measured data and a dynamically iterative supercapacitor energy cost coefficient, accurate quantification of stack performance differences and real-time tracking of energy storage status are achieved. This modeling method improves the accuracy and adaptability of power allocation and optimizes system energy efficiency.

[0053] As an optional embodiment, the step of calculating the optimal power point of the dual-stack reactor specifically includes: The optimal power distribution ratio between fuel cell 1 and fuel cell 2 is obtained by taking the partial derivative of the joint efficiency function. The fusion power consumption function is constructed to express the power consumption characteristics of the dual-stack system in a unified manner, where the fusion coefficient is calculated by a specific formula.

[0054] In this embodiment of the invention, the specific implementation method for calculating the optimal power point of the dual-stack configuration includes: obtaining the partial derivatives of the joint efficiency function with respect to the output power of fuel cell 1 and fuel cell 2 respectively, and establishing a system of equations by setting the partial derivatives equal to zero to solve for the optimal power allocation. The optimal power of fuel cell 1 is calculated using a formula, where the numerator contains a linear combination of the power consumption coefficient of fuel cell 2 and the net output power of the system, and the denominator is a weighted sum of the power consumption coefficients of the two stacks; the optimal power of fuel cell 2 is calculated using a similar method, but the numerator is changed to a linear combination of the power consumption coefficient of fuel cell 1 and the net output power of the system.

[0055] The specific implementation method for constructing the fused power consumption function includes: uniformly expressing the power consumption characteristics of the two stacks as a single-variable quadratic function, where the coefficients of the quadratic term are obtained by harmonic calculation of the power consumption coefficients of the two stacks, the coefficients of the linear term are obtained by weighted average of the power consumption coefficients of the two stacks, and the constant term is obtained by subtracting the correction term from the original constant term combination. This fused function fully preserves the power consumption characteristics of the two stacks, providing a simplified calculation basis for top-level power allocation.

[0056] As can be seen, by obtaining a closed-form solution through partial derivatives of the joint efficiency function and constructing a fused power consumption function, rapid and accurate calculation of the optimal power point of the dual-stack system and effective integration of power consumption characteristics are achieved. This method improves the computational efficiency of power allocation and the overall system performance.

[0057] As an optional embodiment, the step of calculating the optimal output power of the hydrogen power system specifically includes: Establish a total power consumption function that includes the dual-stack fusion power consumption function and the supercapacitor internal resistance model; The optimal output point is determined based on the DC bus power demand, supercapacitor terminal voltage, and internal resistance. Calculate the supercapacitor compensation power to achieve dynamic power matching.

[0058] In this embodiment of the invention, the specific implementation method for calculating the optimal output power of the hydrogen power system includes: establishing a total power consumption function that includes a dual-reactor fusion power consumption function and a supercapacitor internal resistance model. The fusion power consumption function expresses the power consumption characteristics of the dual-reactor system in quadratic function form, and the supercapacitor internal resistance model constructs a power consumption calculation formula based on an internal resistance value of 6.3 milliohms and a measured terminal voltage value. The total power consumption function weights and sums the power consumption of the hydrogen power system and the supercapacitor power consumption using an energy cost coefficient to form a complete mathematical expression of the total system power consumption.

[0059] The specific process for determining the optimal output point based on the DC bus power demand, supercapacitor terminal voltage, and internal resistance is as follows: The partial derivative of the total power consumption function with respect to the hydrogen power system output power is calculated, and an equation is established by setting the derivative to zero. Combined with the power balance equation, an analytical expression for the optimal output power of the hydrogen power system is derived. This expression includes parameters such as the power demand, supercapacitor terminal voltage, internal resistance, and coefficients of the fusion function. The optimal operating point can be calculated by substituting these parameters with real-time measurements.

[0060] The specific method for calculating the supercapacitor compensation power is as follows: the difference between the DC bus demand power and the optimal output power of the hydrogen power system is used to obtain the compensation power value that the supercapacitor needs to provide, thereby achieving dynamic power matching and system power balance.

[0061] It can be seen that by establishing a total power consumption function that integrates the power consumption function and the internal resistance model, and solving for the optimal output point, the total energy consumption of the system is accurately optimized.

[0062] As an optional embodiment, in the step described, the underlying controller outputting the optimal dual-stack power to the actuator during a control cycle specifically includes: The optimal power command for fuel cell 1 and fuel cell 2 is output according to the set control cycle; Real-time acquisition of dual-reactor operating status parameters, including output power and system efficiency, and dynamic adjustment of power allocation strategy.

[0063] In this embodiment of the invention, the specific implementation of the underlying controller outputting the optimal power of the dual-fuel stack to the actuator according to a control cycle includes: the underlying controller executes power output commands according to a set control cycle, which is set to a time interval of no more than 1 millisecond. At the beginning of each control cycle, the controller first reads the calculated optimal power values, including the optimal power of fuel cell 1 and the optimal power of fuel cell 2, and then converts these power values ​​into corresponding control signals and outputs them to the actuator. The actuator adjusts the output power of the fuel cell according to the received control signals to ensure the accuracy and timeliness of the power output.

[0064] The specific process for real-time acquisition of dual-stack operating status parameters is as follows: Sensors installed in the fuel cell system monitor the operating status of both stacks in real time. The acquired parameters include the actual output power of each fuel cell, system efficiency, voltage, and current values. The acquired data is transmitted to the underlying controller via a data acquisition system. The controller dynamically evaluates the system's operating status based on these real-time parameters. When a power distribution deviation or efficiency decrease is detected, the controller automatically adjusts the power distribution strategy, recalculates the optimal power point, and updates the output commands.

[0065] It is evident that by setting precise control cycles and collecting operating parameters in real time, timely output of power commands and continuous monitoring of system status are achieved.

[0066] As an optional embodiment, in the above steps, the top-level controller synchronously outputs power distribution commands between the hydrogen power system and the supercapacitor, specifically including: Simultaneously output the optimal power value of the hydrogen power system and the supercapacitor compensation power value; The power output limit range is set according to the extreme value of the supercapacitor voltage, and overvoltage protection is implemented.

[0067] In this embodiment of the invention, the specific implementation of the top-level controller synchronously outputting power allocation commands for the hydrogen power system and the supercapacitor includes: the top-level controller synchronously outputting the optimal power value of the hydrogen power system and the compensation power value of the supercapacitor in each control cycle. The optimal power value of the hydrogen power system is calculated in real time using a cost-power-consumption algorithm, and the compensation power value of the supercapacitor is determined by the difference between the DC bus power requirement and the optimal power value of the hydrogen power system. The control cycle is set to no more than 1 millisecond to ensure the synchronization and timeliness of the power commands. During the output process, the controller converts the power values ​​into corresponding control signals, and a digital signal processor transmits the commands in parallel to the execution units of the hydrogen power system and the supercapacitor.

[0068] The specific method for setting the power output limit range based on the supercapacitor's voltage extreme values ​​is as follows: The supercapacitor's terminal voltage is monitored in real time. When the voltage is below the minimum threshold of 540 volts or above the maximum threshold of 720 volts, the power distribution strategy is automatically adjusted. By setting upper and lower limits for power output, the charging and discharging power of the supercapacitor is limited to prevent overcharging or over-discharging. When the voltage approaches its extreme value, the controller dynamically reduces the compensation power value to ensure the supercapacitor operates within a safe voltage range, implementing overvoltage protection.

[0069] It is evident that by using synchronous output power commands and voltage extreme value constraint protection, the power coordination and distribution between the hydrogen power system and the supercapacitor are effectively improved, as well as the system's operational safety is enhanced.

[0070] As an optional embodiment, the steps further include a system parameter configuration step: Set the supercapacitor module capacity to a specific value, the internal resistance to a specific value, and the voltage operating range to a specific range; Set the control cycle to a specific duration to ensure the system's real-time response performance.

[0071] In this embodiment of the invention, the specific implementation of the system parameter configuration step includes: setting the supercapacitor module capacity to 363 farads, internal resistance to 6.3 milliohms, and voltage operating range to 540 volts to 720 volts. These parameters are determined based on the technical specifications of the MUCR360OS supercapacitor module, wherein the lower limit of the voltage operating range ensures the minimum operating voltage requirement of the system, and the upper limit prevents overvoltage damage. The module capacity parameter is used for energy state calculation, and the internal resistance value is used for power consumption model establishment, together constituting the basic operating parameters of the supercapacitor system.

[0072] The control cycle is set to a specific duration of no more than 1 millisecond. This duration is determined by a combination of the digital signal processor's computing power and the system's real-time requirements. The control cycle setting considers the complete processing flow of signal acquisition, algorithm computation, and command output, ensuring that all necessary calculations and decision-making processes are completed within each cycle. This duration balances system response speed and computational accuracy, providing a time basis for real-time control.

[0073] It is evident that by setting a complete supercapacitor parameter system and control cycle duration, standardized configuration of basic operating parameters and optimized guarantee of real-time control performance have been achieved.

[0074] Furthermore, this invention is applicable to the "dual-stack fuel cell + supercapacitor" hybrid power system for medium-capacity rubber-tired buses. Different fuel cell stacks exhibit significant performance differences. The specific steps include: Step 1: Construct a hierarchical control framework of "bottom-level dual-reactor power allocation + top-level hydrogen power / supercapacitor power allocation", and define the joint efficiency function of the dual-reactor and the energy cost coefficient of the supercapacitor; Step 2: Calculate the optimal power point of the dual-stack power source based on power consumption parameter fusion, and calculate the optimal output power of the hydrogen power system based on the cost-power consumption algorithm. Step 3: The bottom-level controller outputs the optimal power of the two reactors to the actuator, and the top-level controller outputs power allocation commands between the hydrogen power system and the supercapacitor, realizing hierarchical collaborative control.

[0075] The method for constructing the joint efficiency function of the two heaps in step one is as follows: The method for constructing the joint efficiency function of the two heaps in step one is as follows:

[0076] In the formula: The overall net output power of the dual-reactor system (kW); The output power of fuel cell 1 is (kW). The output power of fuel cell 2 (kW); , , The power consumption fitting coefficients for fuel cell 1 are denoted as . , , The fitting coefficients for the power consumption of fuel cell 2; The calculation method for the supercapacitor energy cost coefficient in step one is as follows: ; in, This refers to the energy state of a supercapacitor. is the actual power consumption of the supercapacitor during the period [k-1,k], and C is the supercapacitor module capacity (F). These represent the maximum and minimum voltages of the supercapacitor, respectively.

[0077] The calculation method for the optimal power point of the dual-stack reactor in step two is as follows: Optimal power of fuel cell 1: ; Optimal power of fuel cell 2: ; The combined power consumption function of the dual-stack system is: ,in .

[0078] The optimal output power consumption of the hydrogen power system in step two is calculated as follows:

[0079] In the formula: DC bus power requirement (kW); This refers to the internal resistance of the supercapacitor. denoted as the supercapacitor terminal voltage (V); h is the supercapacitor energy cost coefficient; a and b are the dual-stack fusion power consumption function coefficients.

[0080] The specific parameters of the supercapacitor system are as follows: Module name MUCR3600S, number of cells 33 in parallel and 15 in series, module capacity 363F, internal resistance... Voltage range 540V (min.) ~ 720V (max.), maximum current 1000A.

[0081] Layer control control cycle The bottom-level controller and the top-level controller communicate in real time through the DSP, adapting to the hardware-in-the-loop (HIL) testing requirements of the Typhoon hardware-in-the-loop simulation platform.

[0082] This invention ensures that fuel cell 1 operates in the efficiency range of ≥60%, fuel cell 2 operates in the efficiency range of ≥50%, and the overall efficiency of the dual-stack system is improved by ≥3% compared with the single-stack operating mode.

[0083] Step one specifically includes constructing a hierarchical control framework and defining core functions and coefficients: 1. Hierarchical Control Framework Design: The bottom-level control focuses on the coordinated power allocation of the two fuel cells with performance differences, aiming to maximize the overall efficiency of the two stacks; the top-level control focuses on the power matching between the hydrogen power system and the supercapacitor, aiming to quantify the charging and discharging costs of the supercapacitor and minimize the total power consumption of the system. The two control layers form a closed-loop coordination through real-time data interaction.

[0084] 2. Definition of the joint efficiency function for dual-stack fuel cells: Based on the power consumption characteristics of dual-stack fuel cells, a joint efficiency maximization model is established, relating the output power of stacks with different performance characteristics to the overall system efficiency. The function expression is as follows:

[0085] In the formula: The net output power (kW) of the dual-reactor system; The output power of fuel cell 1 is (kW). The output power of fuel cell 2 (kW); , , The power consumption fitting coefficients for fuel cell 1 are denoted as . , , is the power consumption fitting coefficient for fuel cell 2. This coefficient is obtained by fitting actual fuel cell energy consumption test data and can accurately reflect the power consumption characteristics of the stack at different life states.

[0086] 3. Definition of Supercapacitor Energy Cost Coefficient: Based on the principle of energy conservation, this method quantifies the hydrogen energy consumption cost per unit of energy stored in a supercapacitor, addressing the insufficient allocation accuracy caused by traditional algorithms relying on mean parameters. The expression for calculating the cost coefficient h(k) is as follows:

[0087] Among them, the energy state of supercapacitors ; The actual power consumption of the supercapacitor during the period [k-1,k]; C=363F is the supercapacitor module capacity; These are the maximum and minimum voltages of the supercapacitor, respectively. This is used to control the interval time (s). This coefficient can follow the changes in the energy state of the supercapacitor in real time, accurately reflecting the energy consumption cost of the charging and discharging process.

[0088] Step two specifically includes calculating the optimal power point: 1. Optimal Power Calculation for the Dual-Stack System: With the objective of maximizing the joint efficiency of the two stacks, partial derivatives of the joint efficiency function are calculated to obtain closed-form solutions for the optimal power of each stack, ensuring that both stacks operate within their efficient operating range. Optimal power of fuel cell 1: ; Optimal power of fuel cell 2: ; To simplify top-level calculations, a combined power consumption function for the dual-stack system is further constructed:

[0089] The fusion coefficient satisfies: This function integrates the optimal power consumption characteristics of a dual-stack architecture, providing fundamental parameters for top-level power allocation.

[0090] 2. Top-level hydrogen power / supercapacitor optimal power calculation: Combining the dual-reactor fusion power consumption function and the supercapacitor power consumption model, a total system power consumption function is constructed. The optimal output power of the hydrogen power system is then calculated with the goal of minimizing total power consumption. Supercapacitor power consumption model: ,in This refers to the internal resistance of the supercapacitor. System total power consumption function: Combined with the DC bus power demand relationship Taking the partial derivative of the total power consumption function yields the optimal output power consumption of the hydrogen power system:

[0091] The supercapacitor compensation power is Achieve dynamic power matching between the hydrogen power system and the supercapacitor.

[0092] Step three includes: control command output and coordinated control.

[0093] The underlying controller is based on the control cycle. Output dual-stack optimal power The top-level controller synchronously outputs the optimal power of the hydrogen power system to the fuel cell actuator. With supercapacitor power compensation The corresponding execution unit is then connected. Real-time communication between the bottom and top layers is achieved through the DSP to ensure the synchronization and timeliness of control commands, adapting to the needs of hardware-in-the-loop (HIL) testing and actual operating conditions.

[0094] This invention achieves deep correlation of power consumption characteristics between two stacks, combined with a fused power consumption function. Formula for optimal output power of hydrogen power system This method accurately quantifies the charging and discharging costs of supercapacitors and the collaborative losses between the two stacks. Compared to traditional equivalent hydrogen consumption algorithms, the total hydrogen consumption of the system is reduced under the heavy-duty operating conditions of the AW3 medium-capacity rubber-tired bus.

[0095] Based on control cycle The hierarchical collaboration mechanism, through Real-time power allocation between the two reactors, combined with supercapacitor voltage constraint of 540V ≤ ≤720V reduces the output power fluctuation rate of the hydrogen power system. The initial and final voltage deviation of the supercapacitor is almost zero, effectively avoiding overcharging and over-discharging and suppressing bus voltage drops.

[0096] By using the optimal power calculation formula for the dual-stack system, it is ensured that fuel cell 1 operates within an efficiency range of ≥60%, and fuel cell 2 operates within an efficiency range of ≥50%. The superior performance of fuel cell 1 is allocated a larger share of output through the formula, while the load on the less efficient fuel cell 2 is reasonably reduced, thus mitigating the performance difference between the two stacks, decreasing the electrochemical degradation rate of the fuel cells, reducing mechanical wear on the equipment, and lowering the operation and maintenance costs of the power system.

[0097] Highly real-time and practical: Integrating power consumption functions The complex power consumption characteristics of dual-stack architecture are simplified into a single function expression. Combined with real-time iterative calculation of the cost coefficient h(k), the computational load of the controller is reduced by 40% compared to traditional algorithms. The entire method achieves rapid solution of the optimal power point through closed-form solutions, is compatible with embedded platforms such as Typhoon HIL, and can be directly applied to practical control systems without the need for complex offline calibration.

[0098] Figure 4 The graphs showing the polarization characteristics and output power fitting curves of the fuel cell system involved in the embodiments of the present invention illustrate the voltage-current characteristics and power output relationship of the fuel cell under different operating conditions, providing a data basis for power consumption characteristic modeling.

[0099] Figure 5 This is a schematic diagram of the online control process of an embodiment of the present invention, illustrating the complete execution flow of the hierarchical power allocation method, including key steps such as data acquisition, cost coefficient update, bottom-level power allocation, and top-level power allocation.

[0100] Figure 6 This is a schematic diagram of the topology of a dual-stack hybrid power system according to an embodiment of the present invention, which clearly shows the electrical connection relationship and control signal flow between the dual-stack fuel cells, supercapacitors, DC / DC converters and loads.

[0101] Figure 7 This is a schematic diagram of the efficiency distribution of a hydrogen power system according to an embodiment of the present invention. The system efficiency distribution characteristics under different power allocation ratios are presented in the form of a contour plot, providing a visual reference for optimized control.

[0102] Example 2 Please see Figure 2 , Figure 2 This is a schematic diagram of a hierarchical power distribution system for a dual-stack fuel cell disclosed in an embodiment of the present invention. Figure 2 The described dual-stack fuel cell hierarchical power distribution system can be applied to data processing chips, processing terminals, or processing servers, and the processing server can be a local server or a cloud server; this embodiment of the invention does not limit the application. Figure 2 As shown, the dual-stack fuel cell hierarchical power distribution system may include: The underlying control module 201 is configured to perform dual-stack power allocation calculations and joint efficiency function solving.

[0103] Specifically, the hierarchical control framework focuses on maximizing the joint efficiency of the dual-stack fuel cell through the bottom-level controller, achieving rapid solution and real-time control of the power allocation algorithm, thus improving the system's practicality and response speed. The design of this invention enables the control strategy to have a definite closed-form solution, effectively adapting to the needs of dynamic operating conditions.

[0104] It is evident that the top-level controller, with the goal of minimizing the total power consumption of the system, optimizes the power matching between the hydrogen power system and the supercapacitor, improves the allocation accuracy and energy efficiency, and enhances the overall operational stability.

[0105] Top-level control module 202 is configured to handle power matching and energy cost factor updates for the hydrogen power system and supercapacitor.

[0106] Specifically, by defining a joint efficiency function for the two fuel cells, a unified efficiency optimization model is established by mathematically correlating the power consumption characteristics of fuel cells with different performance levels. This allows the output power of the two fuel cells to be adjusted collaboratively, thereby improving the overall system efficiency. The functional expression method of this invention overcomes the problem of insufficient coordination of fuel cell performance differences in traditional methods, and achieves deep integration of power consumption characteristics.

[0107] As can be seen, establishing a mathematical model based on the actual power consumption characteristics of fuel cells and using coefficients fitted with measured data accurately reflects the energy consumption patterns of the fuel cell stack under different lifespan states, providing a precise computational basis for power allocation. This model supports closed-form solution solving, enhancing the practicality and real-time performance of the algorithm and optimizing control performance under dynamic operating conditions.

[0108] The data communication module 203 is used to realize real-time data exchange and command synchronization between controllers.

[0109] Specifically, the supercapacitor energy cost coefficient is defined and iteratively calculated based on the energy state, enabling dynamic tracking of the supercapacitor's charging and discharging process and improving the real-time performance and adaptability of power allocation. The iterative mechanism of this invention continuously optimizes the accuracy of energy state assessment and enhances the system's responsiveness to changes in operating conditions.

[0110] As can be seen, calculating the energy state through voltage extrema ensures the reliability of energy storage level quantification, providing a technical foundation for the accurate quantification of hydrogen energy costs. This method optimizes the energy consumption efficiency of power matching by directly correlating voltage parameters with the actual energy storage state.

[0111] The actuator drive module 204 is used to receive control commands and drive the fuel cell and supercapacitor unit to work together.

[0112] Specifically, by taking the partial derivative of the joint efficiency function to obtain a closed-form solution, efficient calculation of the optimal power point for the dual-stack power distribution is achieved, improving the real-time performance and response speed of the control algorithm. This method avoids the complex iterative process of traditional optimization algorithms, enabling power allocation to quickly adapt to dynamic operating conditions.

[0113] As can be seen, the application of the closed-form solution ensures the accuracy and reliability of power distribution calculation, optimizes the cooperative output efficiency of the dual-stack fuel cell, enhances the stability of system operation, and provides a more accurate power reference for the power system.

[0114] Furthermore, by combining the fusion power consumption function with the supercapacitor internal resistance model to solve for the optimal output power of the hydrogen power system, the total power consumption of the system is accurately optimized, improving the overall efficiency of energy management. This invention comprehensively considers the power consumption characteristics of the dual-stack and the internal resistance loss of the capacitor, providing a more comprehensive calculation basis for power allocation. The underlying controller outputs the optimal power of the dual stacks to the actuators according to the set control cycle, ensuring the real-time and synchronous nature of the power distribution command and improving the system's responsiveness to dynamic operating conditions. This periodic output mechanism of the present invention enables power adjustment to keep up with changes in system status in a timely manner, thereby enhancing the timeliness of control. The top-level controller synchronously outputs power distribution commands to the hydrogen power system and the supercapacitor, realizing dynamic power matching between the two energy units and improving the coordination of system energy management. By synchronously outputting control commands, timing deviations in the power distribution process are effectively avoided, enhancing the stability and reliability of system operation.

[0115] Example 3 Please see Figure 3 , Figure 3 This is a schematic diagram of another dual-stack fuel cell hierarchical power distribution system disclosed in an embodiment of the present invention. Figure 3 As shown, the device may include: Memory 301 storing executable program code; Processor 302 coupled to memory 301; The processor 302 calls the executable program code stored in the memory 301 to execute some or all of the steps in the hierarchical power allocation method for a dual-stack fuel cell disclosed in Embodiment 1 of the present invention.

[0116] Example 4 This invention discloses a computer storage medium storing computer instructions. When these computer instructions are invoked, they are used to execute some or all of the steps in the hierarchical power allocation method for a dual-stack fuel cell disclosed in Embodiment 1 of this invention.

[0117] Example 5 This invention discloses a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to perform the steps of a dual-stack fuel cell hierarchical power distribution method described in Embodiment 1.

[0118] The system embodiments described above are merely illustrative. The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical modules; that is, they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0119] Through the detailed description of the above embodiments, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, including read-only memory (ROM), random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), one-time programmable read-only memory (OTPROM), electrically-Erasable Programmable Read-Only Memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0120] Finally, it should be noted that the dual-stack fuel cell hierarchical power distribution method and system disclosed in the embodiments of the present invention are merely preferred embodiments of the present invention, and are only used to illustrate the technical solutions of the present invention, not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for hierarchical power allocation in a dual-stack fuel cell, characterized in that, The method includes: A hierarchical control framework is constructed, which includes a bottom-level dual-reactor power distribution controller and a top-level hydrogen power system / supercapacitor power distribution controller. The bottom-level controller aims to maximize the joint efficiency of the two reactors, while the top-level controller aims to minimize the total power consumption of the system. Define a dual-stack joint efficiency function and establish a mathematical model based on the actual power consumption characteristics of fuel cells, where the power consumption fitting coefficient of fuel cell 1 is a specific value and the power consumption fitting coefficient of fuel cell 2 is a specific value. Define the supercapacitor energy cost coefficient and perform iterative calculations based on the energy state, where the energy state is calculated through voltage extrema. Calculate the optimal power point of the dual-stack system and obtain a closed-form solution by taking the partial derivative of the joint efficiency function. The optimal output power of the hydrogen power system is calculated by combining the fusion power consumption function and the supercapacitor internal resistance model. The underlying controller outputs the optimal power of the dual stacks to the actuators according to the control cycle; The top-level controller synchronously outputs power distribution commands between the hydrogen power system and the supercapacitor.

2. The method for hierarchical power allocation in a dual-stack fuel cell according to claim 1, characterized in that, The hierarchical control framework, comprising a bottom-level dual-reactor power distribution controller and a top-level hydrogen power system / supercapacitor power distribution controller, includes: The underlying controller is configured to handle the coordinated power distribution between the two fuel cells, using a specific control cycle; A top-level controller is set up to handle the power matching between the hydrogen power system and the supercapacitor, so as to achieve dynamic power balance. Establish real-time data communication between the bottom and top level controllers, and achieve data synchronization through the DSP processor.

3. The method for hierarchical power allocation in a dual-stack fuel cell according to claim 1, characterized in that, The definition of the dual-heap joint efficiency function specifically includes: A joint efficiency calculation model is established based on actual test data of dual-stack fuel cells, where the power consumption fitting coefficient of fuel cell 1 is a specific combination of values, and the power consumption fitting coefficient of fuel cell 2 is a specific combination of values. The definition of the supercapacitor energy cost coefficient specifically includes: calculating the energy state based on the measured voltage value of the supercapacitor and the preset voltage extreme value, and iteratively updating it in combination with the module capacity parameters.

4. The method for hierarchical power allocation in a dual-stack fuel cell according to claim 1, characterized in that, The calculation of the optimal power point of the dual-stack system specifically includes: The optimal power distribution ratio between fuel cell 1 and fuel cell 2 is obtained by taking the partial derivative of the joint efficiency function. The fusion power consumption function is constructed to express the power consumption characteristics of the dual-stack system in a unified manner, where the fusion coefficient is calculated by a specific formula.

5. The method for hierarchical power allocation in a dual-stack fuel cell according to claim 1, characterized in that, The calculation of the optimal output power of the hydrogen power system specifically includes: Establish a total power consumption function that includes the dual-stack fusion power consumption function and the supercapacitor internal resistance model; The optimal output point is determined based on the DC bus power demand, supercapacitor terminal voltage, and internal resistance. Calculate the supercapacitor compensation power to achieve dynamic power matching.

6. The method for hierarchical power allocation in a dual-stack fuel cell according to claim 1, characterized in that, The underlying controller outputs the optimal power of the dual stacks to the actuator in a control cycle, specifically including: The optimal power command for fuel cell 1 and fuel cell 2 is output according to the set control cycle; Real-time acquisition of dual-reactor operating status parameters, including output power and system efficiency, and dynamic adjustment of power allocation strategy.

7. A method for hierarchical power allocation in a dual-stack fuel cell according to claim 1, characterized in that, The top-level controller synchronously outputs power distribution commands between the hydrogen power system and the supercapacitor, specifically including: Simultaneously output the optimal power value of the hydrogen power system and the supercapacitor compensation power value; The power output limit range is set according to the extreme value of the supercapacitor voltage, and overvoltage protection is implemented.

8. A method for hierarchical power allocation in a dual-stack fuel cell according to claim 1, characterized in that, It also includes system parameter configuration steps: Set the supercapacitor module capacity to a specific value, the internal resistance to a specific value, and the voltage operating range to a specific range; Set the control cycle to a specific duration to ensure the system's real-time response performance.

9. A dual-stack fuel cell hierarchical power distribution system, used to implement the dual-stack fuel cell hierarchical power distribution method according to any one of claims 1-8, characterized in that, The system includes: The underlying control module is configured to perform dual-stack power allocation calculations and joint efficiency function solving. The top-level control module is configured to handle power matching between the hydrogen power system and the supercapacitor, as well as energy cost factor updates. The data communication module is used to realize real-time data exchange and command synchronization between controllers; The actuator drive module is used to receive control commands and drive the fuel cell and supercapacitor units to work together.

10. A tiered power distribution system for a dual-stack fuel cell, characterized in that, The system includes: Memory containing executable program code; A processor coupled to the memory; The processor calls the executable program code stored in the memory to execute a hierarchical power allocation method for a dual-stack fuel cell as described in any one of claims 1-8.

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