A multi-modal control method for a fuel cell heat dissipation system

Abstract

This invention discloses a multi-modal control method for a fuel cell heat dissipation system, belonging to the field of fuel cell thermal management technology. This invention dynamically switches between three control modes—overall control mode, branch control mode, and group control mode—by real-time monitoring of the stack temperature, BOP temperature, and local temperature of the radiator core, combined with system load status and local temperature difference. In the overall control mode, an improved adaptive PID algorithm is used to uniformly adjust the speed of all fans to achieve rapid cooling. In the branch control mode, the fans in the main loop and auxiliary loop are controlled independently, and the allocation of heat dissipation resources is optimized through a fuzzy-adaptive PID composite algorithm. In the group control mode, a fine-grained group adjustment is achieved by predicting the regional temperature based on an LSTM neural network. This invention can significantly reduce energy consumption by 30%-50%, reduce noise by more than 15 dB(A), extend system life by 20%, and improve temperature control accuracy to ±2℃, adapting to all operating conditions.

Description

Technical Field

[0001] This invention relates to the field of fuel cell management, and more specifically to a multimodal control method for a fuel cell heat dissipation system. Background Technology

[0002] During operation, the heat generated by the chemical reactions inside the fuel cell stack needs to be dissipated in a timely manner through a cooling system to prevent the stack from overheating, which could lead to performance degradation, shortened lifespan, or even damage. Traditional cooling systems typically employ fixed control strategies, such as single fan speed control that adjusts the main circuit fan speed based solely on the stack outlet temperature. This lack of independent control over the auxiliary circuit (BOP system) results in large temperature fluctuations in auxiliary equipment (such as air compressors and water pumps), affecting system stability. Furthermore, in a global start-stop mode, the fan runs at full speed or shuts down completely, leading to high power consumption, high noise, and frequent start-stop cycles that accelerate fan mechanical wear.

[0003] In addition, existing technologies also have the following problems: Single temperature detection point: Relying solely on the temperature of the fuel cell stack as a feedback signal, it cannot reflect the local temperature differences within the heat sink core, resulting in uneven heat dissipation.

[0004] High energy consumption: The fan runs at full speed under high load and cannot flexibly reduce speed under low load, resulting in significant energy waste.

[0005] Coarse control: It cannot independently adjust according to the different heat dissipation requirements of the main circuit (fuel cell stack) and auxiliary circuit (BOP), resulting in local overheating or overcooling.

[0006] The contradiction between noise and lifespan: long-term high-speed operation of the fan generates noise pollution, while frequent start-stop cycles shorten the lifespan of the cooling system.

[0007] Therefore, there is an urgent need for a dynamic and precise heat dissipation control method to balance the contradictions between heat dissipation efficiency, energy consumption, noise, and system lifespan. Summary of the Invention

[0008] To address the aforementioned shortcomings in the prior art, this invention provides a multimodal control method for a fuel cell heat dissipation system.

[0009] To achieve the above-mentioned objectives, the technical solution adopted by this invention is as follows: A multimodal control method for a fuel cell heat dissipation system includes the following steps: S1. Real-time monitoring of temperature parameters of the fuel cell system, including stack temperature T. stack BOP temperature T BOP and the local temperature T of the radiator core local ; S2. Dynamically switch the control mode according to the temperature parameter, system load status and local temperature difference. The control modes include Mode 1, Mode 2 and Mode 3. Among them, Mode 1 is the overall control mode, Mode 2 is the shunt control mode, and Mode 3 is the grouped control mode; S3. Under the selected mode, adjust the speed of the cooling fan through the sub-controller of the cooling system to achieve high-efficiency heat dissipation; Among them, the mode switching priority is Mode 1 < Mode 2 < Mode 3, and the fan speed is gradually adjusted according to the preset slope during switching.

[0010] Further, the temperature parameters monitored in S1 are collected in real time by multiple temperature sensors. Among them, the stack temperature T stack is the stack outlet temperature, the BOP temperature T BOP is the temperature of the auxiliary loop equipment, and the local temperature of the radiator core T local is the temperature value of different areas of the radiator.

[0011] Further, S2 specifically includes the following steps: S21. Determine whether the triggering condition of Mode 1 is satisfied. The triggering condition is any one of the following: the stack temperature T stack ≥T1 and the duration t≥t1, the system power P≥P high and the duration t≥t2, or the duration of the cooling system failure t≥t3; if satisfied, switch to Mode 1; S22. If Mode 1 is not triggered, determine whether the triggering condition of Mode 2 is satisfied. The triggering condition is that both are satisfied: the stack temperature is in the medium temperature range T2≤T stack <T1 and the duration t≥t6, and the main and auxiliary loop temperature difference ∣T BOP T stack ∣≥ΔT BOP and the duration t≥t7; if satisfied, switch to Mode 2; S23. If Mode 1 and Mode 2 are not triggered, determine whether the triggering condition of Mode 3 is satisfied. The triggering condition is any one of the following: the stack temperature T stack <T2 and the duration t≥t 11 , the local temperature difference of the radiator core T local,max T local,min ≥ΔT local and the duration t≥t 12 , or the system is in the low power consumption mode and the duration t≥t 13 ; if satisfied, switch to Mode 3; Among them, T1 is the preset high temperature threshold, T2 is the preset low temperature threshold, Phigh is the high - power threshold, ΔT BOP is the temperature difference threshold between the main and auxiliary circuits, ΔT local is the local temperature difference threshold, t1 to t7, t 11 , t 12 , t 13 is the preset state trigger duration threshold, T local,max is the local highest temperature of the radiator core; T local,min is the local lowest temperature of the radiator core.

[0012] Furthermore, the exit condition for Mode 1 in S21 is that both of the following are satisfied: the stack temperature T stack ≤T1 ΔT and the duration t≥t4, and the system power P≤P high ΔP and the duration t≥t5; where ΔT is the temperature hysteresis interval and ΔP is the power hysteresis interval.

[0013] Furthermore, the exit condition for Mode 2 in S22 is any one of the following: the stack temperature T stack <T2 and the duration t≥t8, the temperature difference between the main and auxiliary circuits ∣T BOP T stack ∣<2ΔT BOP and the duration t≥t9, or the local temperature difference of the radiator core T local,max T local,min ≥ΔT local and the duration t≥t 10 , where t8, t9, t 10 are the preset state trigger duration thresholds.

[0014] Furthermore, the exit condition for Mode 3 in S23 is any one of the following: the stack temperature T stack ≥T2 + ΔT and the duration t≥t 14 , the local temperature difference of the radiator core T local,max T local,min <2ΔT local and the duration t≥t 15 , or the system load change rate ΔtΔP>r set and the duration t≥t 16 ; where r set is the load change rate threshold, t 14 , t 15 , t 16 are the preset state trigger duration thresholds.

[0015] Furthermore, the control logic for mode one in S3 employs an improved adaptive PID algorithm to dynamically adjust the overall speed of all fans, controlling the variable... The calculation method is as follows:

[0016] in, For the stack temperature With set temperature deviation, , , These are the proportionality coefficient, integral time constant, and differential time constant, respectively, which vary with time. For the time integral term, its update formula is:

[0017] in, , , These are the initial parameters. , , , , , This is the adaptive adjustment coefficient.

[0018] Furthermore, in S3, the control logic for mode two involves independent adjustment of the main loop fan and the auxiliary loop fan. The main loop employs a fuzzy-adaptive PID composite control algorithm, with the control quantity... The calculation formula is:

[0019] In the formula, , , For the temperature deviation of the fuel cell stack and rate of change of deviation Adjustment amount based on PID parameters output from fuzzy rule table This represents the temperature deviation of the fuel cell stack over time. , , These are the initial parameters. This is the time integral term.

[0020] Furthermore, the control logic for mode three in S3 involves dividing the heat sink into N regions and employing a predictive control algorithm based on an LSTM neural network to predict the temperature model. for:

[0021] In the formula, To predict the step size, The LSTM network mapping function is used to adjust the fan speed.

[0022] In the formula, For the first Fan group Rotation speed at any given moment , For maximum and minimum speeds, , The temperature threshold for speed regulation is dynamically adjusted based on the frequency and amplitude of temperature fluctuations using an adaptive algorithm.

[0023] The present invention has the following beneficial effects: 1) Reduced energy consumption: Through branch and group control, the average power consumption of the fan is reduced by 30% to 50% under low load.

[0024] 2) Noise optimization: In group mode, the fan runs at low speed or intermittently, reducing noise by more than 15dB(A).

[0025] 3) Extended lifespan: Reduced fan running time at full speed reduces mechanical wear and increases system lifespan by 20%.

[0026] 4) Improved control precision: Multi-point temperature detection combined with PID, fuzzy, neural network and other algorithms improves heat dissipation response speed by 40% and local temperature difference ≤2℃.

[0027] 5) Enhanced adaptability: Multi-mode coverage of all operating conditions, maintaining safe stack temperature even under extreme high temperatures. Attached Figure Description

[0028] Figure 1 This is a flowchart of the multimodal control logic of the present invention. Detailed Implementation

[0029] The specific embodiments of the present invention are described below to enable those skilled in the art to understand the present invention. However, it should be understood that the present invention is not limited to the scope of the specific embodiments. For those skilled in the art, various changes are obvious as long as they are within the spirit and scope of the present invention as defined and determined by the appended claims. All inventions utilizing the concept of the present invention are protected.

[0030] This invention relates to a control method for a fuel cell heat dissipation system, such as... Figure 1 As shown, the core of this paper is to propose a multi-modal control method for a fuel cell heat dissipation system, which involves real-time monitoring of the stack temperature T. stack BOP temperature T BOP and the local temperature T of the radiator core localIt dynamically switches between three modes to achieve high-efficiency heat dissipation.

[0031] The fuel cell cooling system consists of multiple cooling fans, each with a temperature sensor in its core. The cooling system is controlled by a cooling system sub-controller FAN, and its control parameter signals are derived from the fuel cell controller FCU.

[0032] Mode 1 (Overall Control Mode): In order to ensure rapid cooling when the fuel cell generates abnormal heat, all fans in the main and auxiliary circuits are adjusted synchronously in this mode. An improved adaptive PID algorithm can be used to dynamically adjust the overall speed.

[0033] Mode 2 (Splitter Control Mode): Building upon Mode 1, to further achieve differentiated regulation of the stack temperature and BOP temperature, and to realize independent temperature control of the fuel cell main loop and auxiliary loop, the fans of the main loop and auxiliary loop of the cooling system can be independently controlled. The fuzzy-adaptive PID composite control algorithm is simple, efficient, and has good adaptive capabilities, making it highly compatible with the control requirements of Mode 2; therefore, the fuzzy-adaptive PID composite control algorithm is adopted.

[0034] Mode 3 (Group Control Mode): If the cooling system is in an environment with uneven temperature distribution, or if more precise fan control is required, the fans can be divided into multiple groups, and fine-grained control can be performed based on the temperature of the corresponding heatsink core area for each group. Since the core temperature of the cooling system must change gradually (there are no abrupt changes), its future temperature can be predicted based on the current temperature and temperature change trend. Therefore, a predictive control algorithm based on LSTM neural networks can be selected to better meet the fan control requirements of the current mode of the cooling system.

[0035] 1) Mode 1 (Overall Control Mode) Triggering conditions (the following conditions will trigger the event): fuel cell stack temperature and duration ; The system enters a high-power operating state (power) ), and duration ; Duration of heat dissipation system failure Forced cooling is required.

[0036] Exit conditions (exit will occur if all of the following conditions are met): fuel cell stack temperature and duration ; System power and duration ;

[0037] in, The preset high temperature threshold; The preset high power threshold, typical value ; Rated power of the fuel cell system; This is the preset hysteresis range; This is the preset power hysteresis range; , , , , The preset state trigger duration threshold; The control logic for Mode 1 is as follows: the fans in both the main and auxiliary circuits are driven by the cooling system sub-controller FAN, and the overall speed is dynamically adjusted through an improved adaptive PID algorithm. This allows the fuel cell stack temperature to rapidly drop to a safe range. The specific control algorithm is as follows:

[0038] in, For the stack temperature With set temperature deviation, , , Let these be the proportional coefficient, integral time constant, and derivative time constant, respectively, which vary with time. Their update formula is:

[0039] in, , , These are the initial parameters. , , , , , This is the adaptive adjustment coefficient. The PID parameters are adjusted in real time based on the temperature deviation and the rate of change of the deviation using the above formula, concentrating heat dissipation resources to achieve rapid cooling and preventing thermal runaway of the fuel cell stack.

[0040] 2) Mode 2 (Splitter Control Mode) Triggering conditions (the following conditions must be met simultaneously to trigger): The fuel cell stack temperature is in the medium temperature range ( ), and duration ; Difference between BOP temperature and stack temperature and duration .

[0041] Exit conditions (exit will occur if any of the following conditions are met): fuel cell stack temperature and duration ; Temperature difference between main and auxiliary circuits and duration ; Local temperature difference detected in the radiator core and duration ; in The preset low temperature threshold; The preset threshold for the difference between BOP temperature and stack temperature; This represents the highest local temperature within the radiator core. This represents the lowest local temperature within the radiator core. This is the preset local temperature difference threshold for the radiator core; , , , , This is a preset threshold for the state trigger duration.

[0042] The control logic for Mode 2 is as follows: the main circuit fan adjusts its speed according to the fuel cell stack temperature, and the auxiliary circuit fan adjusts its speed independently according to the BOP temperature. A fuzzy control algorithm is introduced based on the PID algorithm.

[0043] The input to the main loop fuzzy controller is the fuel cell temperature deviation. and rate of change of deviation The PID parameter adjustment amount is output through the fuzzy rule table. , , An adaptive PID controller updates parameters based on the adjustment amount, controlling the variable... The calculation formula is:

[0044] The auxiliary loop also employs a fuzzy-adaptive PID composite control algorithm, with the input being the BOP temperature deviation. and rate of change of deviation Control quantity The calculation method is similar to that of the main circuit, and the heat dissipation resources are dynamically allocated through independent adjustment.

[0045] This mode can effectively reduce the ineffective power consumption of the auxiliary loop fan and extend the system life under low load.

[0046] 3) Mode 3 (Group Control Mode) Triggering conditions (the following conditions will trigger the event): fuel cell stack temperature and duration ; Local temperature difference in radiator core and duration ; The system is in a low-power mode (such as idling, standby, etc.) for a period of time. .

[0047] in, , , This is a preset threshold for the state trigger duration.

[0048] Exit conditions (triggered when any one of the following conditions is met): fuel cell stack temperature and duration

[0049] Local temperature difference in radiator core and duration ; System load change rate and duration .

[0050] in, The preset system load change rate threshold; , , This is a preset threshold for the state trigger duration.

[0051] The control logic for Mode 3 is as follows: the main circuit heat sink is divided into N regions, each equipped with a temperature sensor, and its temperature is... A predictive control algorithm based on LSTM neural networks is used to predict the temperature of each region. The prediction model is as follows:

[0052] in To predict the step size, This is the mapping function for the LSTM network.

[0053] Based on the predicted temperature and combined with the adaptive hysteresis control strategy, the fan speed adjustment formula is as follows:

[0054] in, For the first Fan group Rotation speed at any given moment , For maximum and minimum speeds, , The temperature threshold for speed regulation is dynamically adjusted based on the frequency and amplitude of temperature fluctuations using an adaptive algorithm.

[0055] The overall mode switching strategy of the heat dissipation system has the following priority: mode 1 < mode 2 < mode 3, which can ensure accurate temperature control and rapid cooling under extreme conditions.

[0056] In addition, the fan speed is gradually adjusted according to a preset slope (e.g., 200 rpm / s) during mode switching to avoid mechanical shock caused by sudden speed changes. Furthermore, an appropriate temperature hysteresis is set at the mode switching boundary to prevent frequent oscillations between modes.

[0057] Example 1: Mode switching under high temperature conditions Scenario: A fuel cell vehicle is climbing a hill continuously in summer, and the stack temperature rises rapidly to 85°C (T1=80°C).

[0058] Control process: The initial state is mode three. When the system detects that the temperature exceeds the limit, it immediately switches from mode three to mode one, and all fans run at full speed.

[0059] The temperature of the fuel cell stack was reduced to 75°C within 10 minutes, and then the mode was switched to mode two.

[0060] Results: The peak temperature of the fuel cell stack is reduced by 5°C compared to traditional control methods, avoiding the risk of thermal runaway. Full-speed operation time accounts for only 15% of the total operating time, resulting in 62% energy savings compared to the traditional full-range, full-speed mode.

[0061] Example 2: Modal 2 Application in Urban Road Conditions Scenario: Vehicles frequently start and stop in congested urban areas, with the fuel cell stack temperature maintained at 65℃ (T2=60℃), and the BOP temperature fluctuating significantly due to the intermittent operation of the air compressor.

[0062] Control Process: The initial state is Mode 3. Upon entering this scenario, it switches to Mode 2. The main circuit fan maintains stack cooling at 60% speed, while the auxiliary circuit fan adjusts its speed between 30% and 80% based on the BOP temperature. When the BOP temperature drops below 50°C, the auxiliary fan stops, and only the main circuit operates at low speed.

[0063] Results: Auxiliary circuit power consumption reduced by 45%, overall vehicle energy consumption reduced by 8%. BOP temperature fluctuation range reduced from ±10℃ to ±3℃.

[0064] Example 3: Modal optimization under low temperature conditions Scenario: In low-temperature winter conditions, the fuel cell stack operates under low load, and a 5°C temperature difference is formed in a local area of ​​the radiator core due to differences in airflow.

[0065] Control Process: The initial state is Mode 3, and Mode 3 operation is maintained upon entering this scene. The heat sink is divided into 4 groups, and the temperature of the 3rd group is detected to be 3°C lower than that of the other groups. The fans in the 3rd group are turned off, and the remaining three groups run at 40% speed, rotating the start and stop of one group every 5 minutes.

[0066] Results: Overall fan power consumption reduced by 70%, noise level dropped from 65dB(A) to 50dB(A). The overall temperature difference of the heatsink core was kept within 2℃ to prevent localized condensation.

[0067] After comprehensive experimental testing, the data indicators are shown in Table 1 below: Table 1 Comprehensive Test Data Indicators

[0068] It can be seen that the multimodal control method of the fuel cell heat dissipation system involved in this solution is effective, which is beneficial to reducing energy consumption, optimizing noise, extending life, improving control accuracy, and enhancing the environmental adaptability of the system.

[0069] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0070] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0071] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0072] Specific embodiments have been used to illustrate the principles and implementation methods of this invention. The descriptions of the embodiments above are only for the purpose of helping to understand the method and core ideas of this invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this invention. Therefore, the content of this specification should not be construed as a limitation of this invention.

[0073] Those skilled in the art will recognize that the embodiments described herein are intended to help the reader understand the principles of the invention, and should be understood that the scope of protection of the invention is not limited to such specific statements and embodiments. Those skilled in the art can make various other specific modifications and combinations based on the technical teachings disclosed in this invention without departing from the spirit of the invention, and these modifications and combinations are still within the scope of protection of this invention.

Claims

1. A multimodal control method for a fuel cell heat dissipation system, characterized in that, Includes the following steps: S1. Real-time monitoring of temperature parameters of the fuel cell system, including stack temperature T. stack BOP temperature T BOP and the local temperature T of the radiator core local ; S2. Based on the temperature parameters, system load status, and local temperature difference, dynamically switch control modes. The control modes include mode one, mode two, and mode three, wherein mode one is the overall control mode, mode two is the branch control mode, and mode three is the group control mode. S3. Under the selected mode, the speed of the cooling fan is adjusted by the cooling system sub-controller to achieve high-efficiency heat dissipation; The mode switching priority is Mode 1 < Mode 2 < Mode 3, and the fan speed is gradually adjusted according to a preset slope during switching.

2. The multimodal control method for a fuel cell heat dissipation system according to claim 1, characterized in that, The temperature parameters monitored in S1 are acquired in real time by multiple temperature sensors, among which the stack temperature T stack The stack outlet temperature (T) is the BOP temperature. BOP The temperature of the auxiliary circuit equipment, and the local temperature T of the radiator core. local These are the temperature values ​​for different areas of the radiator.

3. The multimodal control method for a fuel cell heat dissipation system according to claim 1, characterized in that, S2 specifically includes the following steps: S21. Determine whether the triggering condition for mode one is met. The triggering condition is any one of the following: stack temperature T stack If the system power P ≥ P1 and the duration t ≥ t1, then the system power P ≥ P2. high And the duration t≥t2, or the duration of the heat dissipation system failure t≥t3; if these conditions are met, then switch to mode one; S22. If Mode 1 is not triggered, it is judged whether the triggering conditions of Mode 2 are satisfied. The triggering conditions are simultaneously satisfied as follows: the stack temperature is in the medium temperature range T2 ≤ T stack <T1 and the duration t ≥ t6, and the temperature difference between the main and auxiliary circuits ∣T BOP T stack ∣≥ ΔT BOP and the duration t ≥ t7; if satisfied, switch to Mode 2; S23. If neither Mode 1 nor Mode 2 is triggered, determine whether the trigger conditions for Mode 3 are met. The trigger conditions are any one of the following: the stack temperature T stack < T2 and the duration t ≥ t 11 , the local temperature difference of the radiator core T local,max T local,min ≥ ΔT local and the duration t ≥ t 12 , or the system is in the low-power mode and the duration t ≥ t 13 ; if so, switch to Mode 3; Where T1 is the preset high temperature threshold, T2 is the preset low temperature threshold, and P high For high power threshold, ΔT BOP Temperature difference threshold between main and auxiliary circuits, ΔT local For local temperature difference thresholds, t1 to t7, t 11 t 12 t 13 T is the preset state trigger duration threshold. local,max This represents the highest local temperature within the radiator core; T local,min This represents the lowest local temperature within the radiator core.

4. The multimodal control method for a fuel cell heat dissipation system according to claim 3, characterized in that, The exit condition for mode one in S21 is that the following conditions are met simultaneously: the stack temperature T stack ≤T1 ΔT and duration t≥t4, and system power P≤P high ΔP and duration t≥t5; where ΔT is the temperature hysteresis interval and ΔP is the power hysteresis interval.

5. The multimodal control method for a fuel cell heat dissipation system according to claim 4, characterized in that, The exit condition for Mode 2 in S22 is any one of the following: the stack temperature T stack < T2 and the duration t ≥ t8, the main - auxiliary loop temperature difference ∣T BOP T stack ∣ < 2ΔT BOP and the duration t ≥ t9, or the local temperature difference of the radiator core T local,max T local,min ≥ ΔT local and the duration t ≥ t 10 where t8, t9, t 10 are preset state - trigger duration thresholds.

6. The multimodal control method for a fuel cell heat dissipation system according to claim 5, characterized in that, The exit condition for mode three in S23 is any one of the following: stack temperature T stack ≥T2+ΔT and duration t≥t 14 Local temperature difference T in the radiator core local,max T local,min <2ΔT local And the duration t≥t 15 Or the system load change rate ΔtΔP>r set And the duration t≥t 16 ;where r set t is the load change rate threshold. 14 t 15 t 16 This is a preset threshold for the state trigger duration.

7. The multimodal control method for a fuel cell heat dissipation system according to claim 1, characterized in that, The control logic for mode one in S3 uses an improved adaptive PID algorithm to dynamically adjust the overall speed of all fans, and the control quantity... The calculation method is as follows: in, For the stack temperature With set temperature deviation, , , These are the proportionality coefficient, integral time constant, and differential time constant, respectively, which vary with time. For the time integral term, its update formula is: in, , , These are the initial parameters. , , , , , This is the adaptive adjustment coefficient.

8. The multimodal control method for a fuel cell heat dissipation system according to claim 1, characterized in that, In S3, the control logic for Mode 2 allows for independent adjustment of the main loop fan and the auxiliary loop fan. The main loop employs a fuzzy-adaptive PID composite control algorithm, with the control quantity... The calculation formula is: In the formula, , , For the temperature deviation of the fuel cell stack and rate of change of deviation Adjustment amount based on PID parameters output from fuzzy rule table This represents the temperature deviation of the fuel cell stack over time. , , These are the initial parameters. This is the time integral term.

9. The multimodal control method for a fuel cell heat dissipation system according to claim 1, characterized in that, The control logic for mode three in S3 involves dividing the heat sink into N regions and employing a predictive control algorithm based on an LSTM neural network to predict the temperature model. for: In the formula, To predict the step size, The LSTM network mapping function is used to adjust the fan speed. In the formula, For the first Fan group Rotation speed at any given moment , For maximum and minimum speeds, , The temperature threshold for speed regulation is dynamically adjusted based on the frequency and amplitude of temperature fluctuations using an adaptive algorithm.

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