Fuel cell automobile control method and control system

Abstract

The invention relates to the technical field of fuel automobiles, in particular to a fuel cell automobile control method and system, and the method comprises the steps: obtaining the hydrogen amount of a vehicle-mounted hydrogen storage tank, judging whether the hydrogen amount is greater than or equal to a preset hydrogen amount threshold value or not, and if yes, starting an FCS and supplying power to a fuel cell automobile by the FCS; if the hydrogen amount is smaller than the preset hydrogen amount threshold value, the fuel cell vehicle is controlled to enter a parking state, real-time illumination intensity is collected, and whether the real-time illumination intensity is larger than a preset illumination intensity threshold value or not is judged; if the real-time illumination intensity is larger than or equal to the preset illumination intensity threshold value, a photovoltaic module is started, and the photovoltaic module generates power to drive a PEM electrolytic bath to conduct electrolytic hydrogen production; and if the real-time illumination intensity is smaller than the preset illumination intensity threshold value, switching to a V2X mode, and driving the PEM electrolytic cell to perform electrolytic hydrogen production through power supply of a power grid. According to the invention, the problems of energy management and hydrogen supply of the fuel cell vehicle in a multi-working-condition environment can be solved, and the intelligent degree of energy management of the fuel cell vehicle is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of fuel cell vehicles, and more particularly, to a fuel cell vehicle control method and control system. Background Art

[0002] Fuel cell vehicles (FCEVs) face the following challenges when operating in different environments: 1. Insufficient hydrogen supply: They rely on hydrogen refueling stations and it is difficult to quickly refuel in remote areas or during long-distance driving; 2. Difficult energy management: They cannot efficiently utilize driving or parking time for energy recovery or hydrogen production; 3. Insufficient adaptability of existing fuel cell systems under multiple working conditions: There is an interruption in energy supply during camping or long-term parking; 4. Low utilization rate of photovoltaic panels: They fail to fully utilize solar energy resources to power on-vehicle systems.

[0003] Chinese invention patents such as "A fuel cell vehicle self-increasing range system and control method based on photovoltaic hydrogen production" with application number CN119749283A, "A fuel cell and solar energy combined supply type electric vehicle" with application number CN104627002A, and "A fuel cell vehicle with solar-assisted power generation" with application number CN105329109A all involve technologies for powering fuel cell vehicles through photovoltaic hydrogen production. Additionally, Chinese invention patents such as "A fuel cell vehicle energy consumption calculation method integrating working condition prediction information" with application number CN115935672A, "A fuel cell vehicle energy management method combining working condition prediction and reinforcement learning" with application number CN117644783A, and "A fuel cell electric vehicle energy distribution method and system considering multiple working conditions" with application number CN113147512B all involve technologies for energy management and control of fuel cell vehicles under different working conditions.

[0004] Therefore, for fuel cell vehicles, there are still many technical problems to be urgently solved in their practical applications, and there are still many technical solutions to be proposed. Summary of the Invention

[0005] Based on this, in order to solve the energy management and hydrogen supply problems of fuel cell vehicles in a multi-working condition environment, the present invention provides a fuel cell vehicle control method and control system, and the specific technical solutions are as follows:

[0006] A fuel cell vehicle control method includes the following steps:

[0007] Obtain the hydrogen quantity in the on-vehicle hydrogen storage tank, and determine whether the hydrogen quantity is greater than or equal to a preset hydrogen quantity threshold. If so, start the FCS and supply power to the fuel cell vehicle by the FCS;

[0008] If the amount of hydrogen is less than the preset hydrogen amount threshold, control the fuel cell vehicle to enter the parking state, collect the real-time light intensity, and determine whether the real-time light intensity is greater than the preset light intensity threshold;

[0009] If the real-time light intensity is greater than or equal to the preset light intensity threshold, start the photovoltaic module, and drive the PEM electrolyzer to electrolyze hydrogen through the power generation of the photovoltaic module;

[0010] If the real-time light intensity is less than the preset light intensity threshold, switch to the V2X mode, and drive the PEM electrolyzer to electrolyze hydrogen through the power supply of the power grid.

[0011] The fuel cell vehicle control method can solve the problems of energy management and hydrogen supply of fuel cell vehicles in multi-condition environments by obtaining and judging the hydrogen amount in the on-vehicle hydrogen storage tank, and switching to different working states according to different hydrogen amounts, such as starting the photovoltaic module and switching to the V2X mode for PEM electrolyzer hydrogen production, which improves the intelligent level of energy management of fuel cell vehicles.

[0012] Preferably, the control method further includes:

[0013] Obtain the conversion efficiency, temperature coefficient and total effective light-receiving area of the photovoltaic module;

[0014] Obtain the real-time irradiance, real-time ambient temperature and solar incidence angle;

[0015] Construct a photovoltaic power generation power model according to the conversion efficiency, temperature coefficient, total effective light-receiving area, real-time irradiance, real-time ambient temperature and solar incidence angle;

[0016] Predict the power generation power of the photovoltaic module according to the photovoltaic power generation power model.

[0017] Preferably, the control method further includes:

[0018] Obtain weather prediction data, and obtain the predicted irradiance value and predicted wind speed value for a future time period according to the weather prediction data;

[0019] Construct a dynamic optimization model for the angle of the photovoltaic module according to the predicted irradiance value, predicted wind speed value, real-time ambient temperature and solar incidence angle.

[0020] Preferably, the photovoltaic power generation power model is expressed as P(t) = η·A·G(t)·[1 + α·(T - 25)]·cosθ;

[0021] Among them, P(t) represents the photovoltaic power generation power, η represents the conversion efficiency, α represents the temperature coefficient, A represents the total effective light-receiving area, G(t) represents the real-time irradiance, T represents the real-time ambient temperature, and θ represents the solar incidence angle.

[0022] Preferably, the dynamic optimization model of the photovoltaic module angle is expressed as

[0023] where G(t) represents the predicted irradiance value, V(t) represents the predicted wind speed value, represents the real-time ambient temperature, and Δθ(t) represents the adjustment amount of the solar incidence angle in the previous time period, G max 、T max 、V max respectively represent the preset maximum irradiance value, maximum ambient temperature value, and maximum wind speed value, λ1, λ2, and λ3 respectively represent the weight coefficients of the predicted irradiance value, predicted wind speed value, and real-time ambient temperature, and γ represents the inertia coefficient.

[0024] Preferably, the control method further includes:

[0025] Obtaining the inclination angle adjustment amount Δθ' of the photovoltaic module within a unit time according to the dynamic optimization model of the photovoltaic module angle;

[0026] Obtaining the depreciation cost coefficient k of the photovoltaic module;

[0027] Constructing an objective function J = P(t) - k(Δθ') according to the photovoltaic power generation, inclination angle adjustment amount, and depreciation cost coefficient 2 .

[0028] A fuel cell vehicle control system for implementing the fuel cell vehicle control method as described above, includes:

[0029] A parameter acquisition module for acquiring the hydrogen quantity in the on-vehicle hydrogen storage tank and the real-time illumination intensity;

[0030] A control module for determining whether the hydrogen quantity is greater than or equal to a preset hydrogen quantity threshold. If so, starting the FCS and supplying power to the fuel cell vehicle by the FCS. If the hydrogen quantity is less than the preset hydrogen quantity threshold, controlling the fuel cell vehicle to enter a parking state and determining whether the real-time illumination intensity is greater than a preset illumination intensity threshold;

[0031] The control module is further configured to start the photovoltaic module when the real-time illumination intensity is greater than or equal to the preset illumination intensity threshold, drive the PEM electrolyzer to electrolyze hydrogen through the power generation of the photovoltaic module, and switch to the V2X mode when the real-time illumination intensity is less than the preset illumination intensity threshold, and drive the PEM electrolyzer to electrolyze hydrogen through the power supply of the power grid.

[0032] Preferably, the parameter acquisition module is further configured to acquire the conversion efficiency, temperature coefficient, total effective light-receiving area, real-time irradiance, real-time ambient temperature, and solar incidence angle of the photovoltaic module, and the control system further includes:

[0033] A power prediction module, configured to construct a photovoltaic power generation model P(t)=η·A·G(t)·[1+α·(T - 25)]·cosθ according to the conversion efficiency η, temperature coefficient α, total effective light-receiving area A, real-time irradiance G(t), real-time ambient temperature T, and solar incidence angle θ, and predict the power generation power of the photovoltaic module according to the photovoltaic power generation model.

[0034] Preferably, the control system further includes:

[0035] An angle optimization module, configured to construct a dynamic optimization model for the angle of the photovoltaic module according to the predicted irradiance value, predicted wind speed value, real-time ambient temperature, and solar incidence angle

[0036] where G(t) represents the predicted irradiance value, V(t) represents the predicted wind speed value, represents the real-time ambient temperature, Δθ(t) represents the adjustment amount of the solar incidence angle in the previous time period, G max , T max , V max respectively represent the preset maximum irradiance value, maximum ambient temperature value, and maximum wind speed value, λ1, λ2, and λ3 respectively represent the weight coefficients of the predicted irradiance value, predicted wind speed value, and real-time ambient temperature, and γ represents the inertia coefficient.

[0037] Preferably, the control system further includes:

[0038] An objective function construction module, configured to construct an objective function J = P(t) - k(Δθ') according to the photovoltaic power generation P(t), the inclination adjustment amount Δθ' of the photovoltaic module per unit time, and the depreciation cost coefficient k of the photovoltaic module 2 . Description of the Drawings

[0039] The present invention can be further understood from the following description in conjunction with the drawings. The components in the drawings are not necessarily drawn to scale, but the emphasis is placed on showing the principles of the embodiments. In different views, the same reference numerals designate corresponding parts.

[0040] Figure 1 is the overall flowchart of a fuel cell vehicle control method in an embodiment of the present invention;

[0041] Figure 2 is the flowchart of a fuel cell vehicle control method in another embodiment of the present invention Figure 1 ;

[0042] Figure 3 is the flowchart of a fuel cell vehicle control method in another embodiment of the present invention Figure 2 ;

[0043] Figure 4 is a schematic flow chart of a fuel cell vehicle control method in another embodiment of the present invention Figure 3 ;

[0044] Figure 5 is a schematic diagram of the overall structure of a fuel cell vehicle control system in an embodiment of the present invention;

[0045] Figure 6 is a schematic diagram of the structure of a fuel cell vehicle control system in another embodiment of the present invention;

[0046] Figure 7 is a schematic flow chart of a fuel cell vehicle control method in another embodiment of the present invention Figure 4 。 Detailed Embodiments

[0047] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below in conjunction with its embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and do not limit the protection scope of the present invention.

[0048] It should be noted that when an element is referred to as being "fixed to" another element, it can be directly on the other element or there may also be an intermediate element. When an element is considered to be "connected" to another element, it can be directly connected to the other element or there may be an intermediate element at the same time. The terms "vertical", "horizontal", "left", "right" and similar expressions used herein are only for the purpose of illustration and do not represent the only embodiments.

[0049] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs. The terms used in the description of the present invention herein are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more of the related listed items.

[0050] The "first" and "second" described in the present invention do not represent specific quantities and sequences, but are only used for name distinction.

[0051] As Figure 1 shown, an embodiment of the present invention provides a fuel cell vehicle control method, including the following steps:

[0052] S1. Obtain the hydrogen quantity of the on-vehicle hydrogen storage tank, and determine whether the hydrogen quantity is greater than or equal to a preset hydrogen quantity threshold. If so, start the FCS (Fuel Cell System) and supply power to the fuel cell vehicle by the FCS.

[0053] Specifically, the preset hydrogen quantity threshold can be preset by technicians. For example, it can be set to 0.5 kg or 1.0 kg, etc.

[0054] S2. If the hydrogen quantity is less than the preset hydrogen quantity threshold, control the fuel cell vehicle to enter the parking state, collect the real-time light intensity, and determine whether the real-time light intensity is greater than the preset light intensity threshold.

[0055] S3. If the real-time light intensity is greater than or equal to the preset light intensity threshold, start the photovoltaic module, and drive the PEM (Proton Exchange Membrane) electrolyzer to electrolyze hydrogen by the power generation of the photovoltaic module.

[0056] The photovoltaic module is preferably a photovoltaic panel. Drive the PEM (Proton Exchange Membrane) electrolyzer to electrolyze hydrogen by the power generation of the photovoltaic module until the hydrogen quantity in the hydrogen storage tank is greater than the preset hydrogen storage threshold, such as 2.0 kg or 2.5 kg.

[0057] S4. If the real-time light intensity is less than the preset light intensity threshold, switch to the V2X (Vehicle-to-Everything) mode, and drive the PEM electrolyzer to electrolyze hydrogen by power supply from the power grid to achieve bidirectional energy flow between the vehicle and the power grid.

[0058] In the fuel cell vehicle control method, hydrogen is produced by using the on-vehicle PEM electrolyzer to realize a closed loop of hydrogen production and hydrogen storage, which can improve the autonomy of the system hydrogen supply; the photovoltaic panel, fuel cell and battery work together to support intelligent switching in multiple scenarios, improving the energy utilization efficiency; the interconnection between the vehicle and the power grid is realized through V2X, which can improve the flexibility and utilization rate of energy; through multi-condition switching, the fuel cell vehicle system supports different scenarios such as long-term parking, driving, and camping, improving the use convenience.

[0059] That is to say, the fuel cell vehicle control method can solve the energy management and hydrogen supply problems of fuel cell vehicles in multi-condition environments and improve the intelligent level of energy management of fuel cell vehicles by obtaining and judging the hydrogen quantity of the on-vehicle hydrogen storage tank and switching to different working states according to different hydrogen quantities, such as starting the photovoltaic module and switching to the V2X mode for PEM electrolyzer hydrogen production.

[0060] In one embodiment of the present invention, asFigure 2 As shown, the control method further includes:

[0061] S5. Obtain the conversion efficiency, temperature coefficient, and total effective light-receiving area of the photovoltaic module.

[0062] S6. Obtain the real-time irradiance, real-time ambient temperature, and solar incidence angle.

[0063] S7. Construct a photovoltaic power generation model based on the conversion efficiency, temperature coefficient, total effective light-receiving area, real-time irradiance, real-time ambient temperature, and solar incidence angle.

[0064] S8. Predict the power generation of the photovoltaic module according to the photovoltaic power generation model.

[0065] Preferably, the photovoltaic power generation model is expressed as P(t) = η·A·G(t)·[1 + α·(T - 25)]·cosθ; where P(t) represents the photovoltaic power generation, η represents the conversion efficiency, α represents the temperature coefficient, A represents the total effective light-receiving area, G(t) represents the real-time irradiance, T represents the real-time ambient temperature, and θ represents the solar incidence angle.

[0066] The conversion efficiency is defined as the theoretical conversion efficiency of the photovoltaic module under standard test conditions (irradiance 1000W / m 2 , temperature 25°C, AM1.5 spectrum), usually 15% - 25%, which is affected by factors such as material type (such as monocrystalline silicon, PERC, TOPCon), production process, etc. The total effective light-receiving area (m 2 ) includes the front and back (if the photovoltaic module is a bifacial module). The real-time irradiance represents the solar radiation power received per unit time and unit area (W / m 2 ), which is affected by weather, season, and geographical location, and the dynamic range is usually 0 - 1200W / m 2 . The real-time irradiance can be monitored in real time by combining meteorological satellite data and ground sensors.

[0067] The temperature coefficient represents the linear coefficient (% / °C) of the conversion efficiency of the photovoltaic module changing with temperature, usually negative (such as -0.3% / °C to -0.5% / °C), indicating that for every 1°C increase in temperature, the efficiency decreases by α%. Specifically, if the real-time ambient temperature = 30°C and α = -0.4% / °C, then the efficiency correction term 1 + α·(T - 25) = 1 + (-0.4)(30 - 25) = 0.8, that is, the conversion efficiency decreases by 20%. The real-time ambient temperature represents the air temperature (°C) of the working environment of the photovoltaic module, which can be monitored in real time by a thermocouple or an infrared sensor. The solar incidence angle represents the angle (degrees) between the sun's rays and the normal of the photovoltaic module.

[0068] The photovoltaic power generation power model P(t) = η·A·G(t)·[1 + α·(T - 25)]·cosθ can be achieved through irradiance and temperature compensation (combining real-time irradiance and real-time ambient temperature to correct the real-time efficiency term η·[1 + α·(T - 25)]) and angle optimization (using a solar tracking algorithm to adjust the solar incident angle to maximize the value of cosθ).

[0069] In the photovoltaic power generation power model P(t) = η·A·G(t)·[1 + α·(T - 25)]·cosθ, through the synergistic effect of variables including real-time irradiance and solar incident angle, high-precision prediction and dynamic optimization of the power generation power of the photovoltaic module can be realized.

[0070] In one embodiment of the present invention, as Figure 3 shown, the control method further includes:

[0071] S9, obtaining weather prediction data, and obtaining the predicted irradiance value and predicted wind speed value for a future time period according to the weather prediction data.

[0072] S10, constructing a dynamic optimization model for the angle of the photovoltaic module according to the predicted irradiance value, predicted wind speed value, real-time ambient temperature, and solar incident angle.

[0073] Preferably, the dynamic optimization model for the angle of the photovoltaic module is expressed as where G(t) represents the predicted irradiance value, V(t) represents the predicted wind speed value, represents the real-time ambient temperature, Δθ(t) represents the adjustment amount of the solar incident angle in the previous time period, G max 、T max 、V max respectively represent the preset maximum irradiance value, maximum ambient temperature value, and maximum wind speed value, λ1, λ2, and λ3 respectively represent the weight coefficients of the predicted irradiance value, predicted wind speed value, and real-time ambient temperature, and γ represents the inertia coefficient.

[0074] Specifically, θ' represents the dynamically optimized solar incident angle, in degrees, and needs to satisfy the mechanical constraint θ min ≤θ'≤θ max ,θ min 、θ max respectively represent the preset minimum solar incident angle and maximum solar incident angle. The predicted irradiance value represents the predicted irradiance (W / m 2 ) for a future time period, which can be obtained through numerical weather prediction or a machine learning model. The predicted wind speed value is used to evaluate the mechanical stability risk, G max 、T max 、V maxAs a normalization parameter for unifying the dimension, the inertia coefficient is used to control the smoothness of the angle change, and usually takes a value of 0.1 - 0.3.

[0075] For λ1, λ2, and λ3, an LSTM neural network can be used to predict the weather pattern in the next 3 hours, and dynamically update the values of λ1, λ2, and λ3. For example, during a thunderstorm, the value of λ2 increases (such as increasing to 0.6 or 0.7, etc.), so that the angle dynamic optimization model of the photovoltaic module preferentially avoids mechanical risks.

[0076] In this embodiment, the gradient descent method or the genetic algorithm can be used to solve the dynamically optimized solar incident angle. By making argmin(1 / cosθ) equivalent to maximizing cosθ, the maximum power generation of the photovoltaic module can be achieved; while restricting the change amplitude of Δθ(t) (such as a single adjustment ≤ 5°), the motor life can be extended and the minimum mechanical loss of the system can be realized.

[0077] The angle dynamic optimization model of the photovoltaic module realizes the collaborative optimization of power generation efficiency and equipment reliability by integrating weather prediction, module characteristics and mechanical constraints, and can improve the comprehensive power generation compared with the traditional fixed angle scheme.

[0078] In one embodiment of the present invention, as Figure 4 shown, the control method further includes:

[0079] S11, obtaining the inclination angle adjustment amount Δθ' of the photovoltaic module per unit time according to the angle dynamic optimization model of the photovoltaic module;

[0080] S12, obtaining the depreciation cost coefficient k of the photovoltaic module;

[0081] S13, constructing an objective function J = P(t) - k(Δθ') according to the photovoltaic power generation, inclination angle adjustment amount and depreciation cost coefficient 2 .

[0082] Among them, if the objective function is a positive term, it represents the power generation income, and if it is a negative term, it represents the loss cost, and the optimal inclination angle adjustment amount Δθ' can be solved by the gradient descent method or the genetic algorithm. The inclination angle adjustment amount can be obtained by collecting the angle change signal of the photovoltaic module through an angle sensor. For the angle change signal, a Kalman filter can be used for filtering to suppress the angle jitter caused by sensor noise and smooth the control signal.

[0083] The depreciation cost coefficient can be regressed through historical maintenance data. Preferably, Among them, represents the inclination angle adjustment amount per unit time in the historical maintenance data, and θ totalIt represents the cumulative inclination adjustment amount (the total adjustment angle during the system operation), which characterizes the cumulative effect of long-term wear. a represents the instantaneous loss coefficient of a single adjustment, which characterizes the impact effect of angle mutation on mechanical components and is determined by regression using the least squares method or the gradient descent method, or set by technicians based on experience. b represents the long-term attenuation coefficient of cumulative wear, which reflects the erosion effect of the total adjustment amount on the lifespan and can be set by technicians based on experience.

[0084] The depreciation cost coefficient It can dynamically reflect the real-time loss state of the mechanical system, quantify the non-linear relationship between the mechanical loss cost coefficient k and the inclination adjustment amount, and provide a quantitative basis for optimizing the objective function.

[0085] The objective function realizes the dynamic balance between the system efficiency and lifespan of the control system through the trade-off between power generation benefits and mechanical loss costs, which can not only increase the total power generation of the system but also extend the lifespan of the mechanical components of the system.

[0086] As Figure 7 shown, an embodiment of the present invention also provides a control method for a fuel cell vehicle, and its energy management and hydrogen production process:

[0087] 1. System startup: The system first detects whether the hydrogen quantity in the hydrogen storage tank is greater than 0.5 kg. If the hydrogen is sufficient (≥0.5 kg), the fuel cell system (FCS) starts to provide power for the vehicle, and the vehicle enters the driving state; if the hydrogen is insufficient, the vehicle enters the parking state.

[0088] 2. Energy management in the parking state:

[0089] Detect whether there is light in the current environment.

[0090] There is light: The photovoltaic panel starts to supply power to the electrolyzer for hydrogen production. When the hydrogen storage reaches 2 kg or the light is insufficient, the photovoltaic system is turned off and the vehicle enters the driving state.

[0091] There is no light: The system switches to the V2X mode and hydrogen is produced by power supply from the power grid. When the hydrogen storage reaches 2 kg, the V2X interface is turned off and the vehicle is ready to drive.

[0092] 3. Driving state: Once the hydrogen reserve reaches 2 kg, the system stops hydrogen production and the vehicle enters the driving state, with the fuel cell system providing power.

[0093] This system ensures that the vehicle can intelligently replenish hydrogen when the hydrogen storage is low and realizes efficient energy management under various working conditions through the coordinated operation of the photovoltaic panel, V2X interface, and electrolytic hydrogen production system.

[0094] As Figure 5As shown, an embodiment of the present invention further provides a fuel cell vehicle control system for implementing the fuel cell vehicle control method described above, including a parameter acquisition module and a control module.

[0095] The parameter acquisition module is used to acquire the hydrogen quantity in the on-vehicle hydrogen storage tank and the real-time light intensity; the control module is used to determine whether the hydrogen quantity is greater than or equal to a preset hydrogen quantity threshold. If so, it starts the FCS and the FCS powers the fuel cell vehicle. If the hydrogen quantity is less than the preset hydrogen quantity threshold, it controls the fuel cell vehicle to enter a parking state and determines whether the real-time light intensity is greater than a preset light intensity threshold.

[0096] The control module is also used to start the photovoltaic module when the real-time light intensity is greater than or equal to the preset light intensity threshold, generate electricity through the photovoltaic module to drive the PEM electrolyzer for hydrogen production, and switch to the V2X mode when the real-time light intensity is less than the preset light intensity threshold, and drive the PEM electrolyzer for hydrogen production by power supply from the power grid.

[0097] Specifically, for the PEM electrolyzer, its power is 76.5 kW and the hydrogen production rate is 15 Nm / h. The reason for selection: To produce 1 kg of hydrogen per hour, a 76.5 kW PEM electrolyzer is required, providing an electrode area of approximately 2.55 m 2 The hydrogen production of this type of electrolyzer is about 15 Nm / h, and the overall volume is 0.36 m 3 The PEM electrolyzer can be placed in the trunk of the fuel vehicle, leaving some space for other equipment, fully meeting the requirements to ensure the continuity of vehicle operation.

[0098] The control system also includes a compressor. For the compressor, it compresses hydrogen from 10 MPa to 35 MPa at a flow rate of 15 Nm / h. The power requirement of the compressor is about 0.7 kW, and a small multi-stage booster pump is suitable for use. The output pressure of the compressor is 35 MPa. The reason for selection: It can ensure that 5 kg of hydrogen is stored in the high-pressure tank, reducing the volume occupation.

[0099] For the hydrogen storage tank, its capacity is 5 kg of hydrogen, and the energy density of hydrogen is 33.33 kWh / kg. The provided energy: 5 kg of hydrogen can provide 5 kg × 33.33 kWh / kg = 166.65 kWh of energy. The hydrogen storage capacity of 5 kg can provide the vehicle with a cruising range of at least 500 km, meeting the daily travel and camping needs, while reducing the volume of the hydrogen storage tank and saving the interior space of the vehicle.

[0100] The power of the fuel cell system (FCS) is 100 kW, which is used to provide vehicle driving and power supply requirements. The control system also includes a battery system, whose capacity is 2 kWh, which is used for vehicle start-up, power compensation for rapid acceleration and deceleration, and braking energy recovery.

[0101] The photovoltaic module uses a perovskite-silicon tandem cell with an efficiency of over 40%. Its advantages include: reducing the installation area, enabling high-efficiency power generation to be integrated even on a smaller roof or vehicle body surface. The photovoltaic module can be installed in the form of segmented modular photovoltaic panels. A 100-square-meter photovoltaic panel is split into several small pieces, which are unfolded when needed and supported by the roof, trunk, or tent brackets on the vehicle side. The area of the photovoltaic module is 100 m 2 , and the power generation capacity is 400 W / m 2 . The daily power generation is 100 m 2 ×400 W / m 2 ×5 h = 200 kwh.

[0102] Using vehicle-to-grid (V2G) charging technology, the vehicle can connect to the grid to produce hydrogen when parked or during off-peak electricity prices, enabling bidirectional energy flow between the vehicle and the grid.

[0103] The control system also includes a water tank with a preparation capacity of 100 L. Approximately 60.26 kg of water is required to produce 75 Nm 3 of hydrogen. The water consumption during the electrolysis process is usually slightly higher than the theoretical value because some water is also needed for cooling and removing impurities in the system. The water comes from the operation of the fuel cell system. Therefore, 65 - 70 kg of water needs to be prepared in the actual system to ensure continuous operation.

[0104] The volume of the water tank is: 0.5 m × 0.4 m × 0.5 m = 0.1 m 3 . The specific dimensions of the water tank can be selected according to the vehicle's space arrangement, and a cuboid shape is more reasonable.

[0105] In the control system of the fuel cell vehicle, the photovoltaic power generation process is as follows: The system uses modular photovoltaic panels with a total area of 100 m 2 , which are split into several small pieces and installed on the roof, trunk, and tent brackets on the vehicle side. The power generation efficiency of the photovoltaic panels is set at 400 W / m 2 . Under ideal lighting conditions (5 hours per day): The photovoltaic power generation amount E 光伏 = 100 m 2 ×400 W / m 2 ×5 h = 200 kWh.

[0106] The photovoltaic power supply process is as follows: When the light is sufficient, the photovoltaic module preferentially supplies power to the PEM electrolyzer for hydrogen production. If the light is insufficient, the system prompts the user to switch to the V2X mode and use grid power supply.

[0107] Hydrogen production process: The reaction equation of the PEM electrolyzer is: 2H2O → H2 + O2. For every 1 mole of hydrogen produced, 1 mole of water (18 g) is required.

[0108] Calculation of the amount of water for hydrogen production: The required amount of water is 670 mol × 18 g / mol = 12.06 kg / h.

[0109] The hydrogen compression process is as follows: The hydrogen is compressed from 10 MPa to 35 MPa. For every Nm 3 The energy consumption of hydrogen is approximately 0.7 kW: P 压缩 = 0.7 kW × 15 Nm3 / h = 10.5 kW.

[0110] The fuel cell power generation process is as follows: The fuel cell converts the energy of 5 kg of hydrogen into electrical energy (assuming an efficiency of 60%). E 输出 = 166.65 kWh × 0.6 = 99.99 kWh, which is sufficient to support the vehicle for long - distance driving or power supply.

[0111] The working process of the fuel cell vehicle control system is as follows:

[0112] 1. System startup: Check whether the hydrogen in the hydrogen storage tank is less than 0.5 kg. If insufficient, enter the hydrogen production mode;

[0113] 2. Hydrogen production mode: The PEM electrolyzer starts, and the compressor compresses the hydrogen to 35 MPa and stores it in a 5 - kg hydrogen storage tank;

[0114] 3. Driving mode: The fuel cell provides driving force and charges the battery through fuel cell power generation or the braking energy recovery system;

[0115] 4. Camping mode: The photovoltaic panel powers the PEM electrolyzer to produce and store hydrogen.

[0116] 5. V2X mode: Bidirectional energy exchange occurs when the vehicle is connected to the power grid.

[0117] In summary, the fuel cell vehicle control system can solve the problems of energy management and hydrogen supply of fuel cell vehicles in multi - working - condition environments by obtaining and judging the hydrogen amount in the on - vehicle hydrogen storage tank, and switching to different working states according to different hydrogen amounts, such as starting the photovoltaic module and switching to the V2X mode for PEM electrolyzer hydrogen production, improving the intelligent level of energy management of fuel cell vehicles.

[0118] In an embodiment of the present invention, as Figure 6As described above, the fuel cell vehicle control system realizes the energy self - sufficiency and efficient management of the vehicle through the integration of a fuel cell (FCS), a PEM electrolyzer, a compressor, a hydrogen storage tank, a photovoltaic module, a battery system BAT, and a V2X interface, as follows:

[0119] 1. Hydrogen production and storage: The photovoltaic module generates electricity using solar energy or obtains electrical energy from the power grid through the V2X interface. The alternating current is converted into direct current through a transformer and an AC / DC converter to provide power for the electrolyzer. The electrolyzer uses water to produce hydrogen, with part of it coming from the recovery of the water generated by the reaction. The generated hydrogen is compressed to 35 MPa by a 0.7 - kW compressor and stored in high - pressure hydrogen cylinders for use in the fuel cell.

[0120] 2. Fuel cell power generation: During driving, hydrogen enters the 100 - kW fuel cell system (FCS) from the hydrogen storage tank, reacts with oxygen to generate electrical energy, and at the same time, the generated reaction water flows back to the water tank. The electrical energy of the fuel cell is converted into the electrical energy required by the drive motor through an inverter to propel the vehicle forward.

[0121] 3. Battery system and auxiliary energy management: The excess electrical energy of the fuel cell is stored in the 2 - kWh battery system for starting or short - term acceleration. The battery system and the fuel cell and the motor perform bidirectional charge and discharge through a DC / DC conversion module.

[0122] 4. Energy interaction and V2X mode: When the vehicle is parked or camping, the vehicle exchanges energy with the power grid or other devices through the V2X interface to achieve energy feedback and external power supply.

[0123] In summary, the fuel cell vehicle control system can achieve:

[0124] 1. Energy self - sufficiency: Through the integration of photovoltaic, electrolyzer, and hydrogen storage tank, a closed - loop system for hydrogen production and storage is achieved.

[0125] 2. Multi - scenario adaptability: It supports different working conditions such as long - distance driving, camping, and parking.

[0126] 3. Efficient energy interaction: V2X technology makes the energy utilization between the vehicle and the power grid more flexible.

[0127] 4. Reducing dependence on hydrogen refueling stations: The on - vehicle hydrogen production function improves the vehicle's endurance and autonomy.

[0128] In an embodiment of the present invention, the parameter acquisition module is further configured to acquire the conversion efficiency, temperature coefficient, total effective light - receiving area, real - time irradiance, real - time ambient temperature, and solar incident angle of the photovoltaic module, and the control system further includes a power prediction module.

[0129] The power prediction module is used to construct a photovoltaic power generation model P(t) = η·A·G(t)·[1 + α·(T - 25)]·cosθ based on the conversion efficiency η, temperature coefficient α, total effective light-receiving area A, real-time irradiance G(t), real-time ambient temperature T, and solar incidence angle θ, and predict the power generation of the photovoltaic module according to the photovoltaic power generation model.

[0130] In the photovoltaic power generation model P(t) = η·A·G(t)·[1 + α·(T - 25)]·cosθ, through the synergistic effect of variables including real-time irradiance and solar incidence angle, high-precision prediction and dynamic optimization of the power generation of the photovoltaic module can be achieved.

[0131] In an embodiment of the present invention, the control system further includes an angle optimization module.

[0132] The angle optimization module is used to construct a dynamic angle optimization model of the photovoltaic module according to the predicted irradiance value, predicted wind speed value, real-time ambient temperature, and solar incidence angle where G(t) represents the predicted irradiance value, V(t) represents the predicted wind speed value, represents the real-time ambient temperature, Δθ(t) represents the adjustment amount of the solar incidence angle in the previous time period, G max 、T max 、V max respectively represent the preset maximum irradiance value, maximum ambient temperature value, and maximum wind speed value, λ1, λ2, and λ3 respectively represent the weight coefficients of the predicted irradiance value, predicted wind speed value, and real-time ambient temperature, and γ represents the inertia coefficient.

[0133] The gradient descent method or genetic algorithm can be used to solve the dynamically optimized solar incidence angle. By argmin(1 / cosθ) being equivalent to maximizing cosθ, the maximum power generation of the photovoltaic module can be achieved; and by restricting the change range of Δθ(t) (such as a single adjustment ≤ 5°), the service life of the motor can be extended and the mechanical loss of the system can be minimized.

[0134] The dynamic angle optimization model of the photovoltaic module realizes the collaborative optimization of power generation efficiency and equipment reliability by integrating weather prediction, module characteristics, and mechanical constraints, and can improve the comprehensive power generation compared with the traditional fixed angle scheme.

[0135] In one embodiment of the present invention, the control system further includes an objective function construction module.

[0136] The objective function construction module is used to construct an objective function J = P(t) - k(Δθ') according to the photovoltaic power generation P(t), the inclination adjustment amount Δθ' of the photovoltaic module per unit time, and the depreciation cost coefficient k of the photovoltaic module 2 .

[0137] The depreciation cost coefficient can dynamically reflect the real-time loss state of the mechanical system, quantify the non-linear relationship between the mechanical loss cost coefficient k and the inclination adjustment amount, and provide a quantitative basis for the optimization of the objective function.

[0138] The objective function realizes the dynamics of the system efficiency and life of the control system through the trade-off between power generation revenue and mechanical loss cost.

[0139] In summary, the fuel cell vehicle control method and system have the following beneficial effects:

[0140] 1. Energy self-sufficiency: By integrating photovoltaic with electrolyzers and hydrogen storage tanks, a closed-loop system for hydrogen production and storage is realized.

[0141] 2. Multi-scenario adaptability: Supports different working conditions such as long-distance driving, camping, and parking.

[0142] 3. Efficient energy interaction: V2X technology makes the energy utilization between vehicles and the power grid more flexible.

[0143] 4. Reducing dependence on hydrogen refueling stations: The on-vehicle hydrogen production function improves the vehicle's endurance and autonomy.

[0144] 5. Through the synergistic effect of variables including real-time irradiance and solar incident angle, high-precision prediction and dynamic optimization of the power generation power of photovoltaic modules can be achieved.

[0145] 6. By integrating weather prediction, module characteristics, and mechanical constraints, the synergistic optimization of power generation efficiency and equipment reliability is realized, and the comprehensive power generation can be improved compared with the traditional fixed-angle scheme.

[0146] 7. The objective function realizes the dynamics of the system efficiency and life of the control system through the trade-off between power generation revenue and mechanical loss cost.

[0147] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope recorded in this specification.

[0148] The above embodiments only represent several implementation manners of the present invention. The description is relatively specific and detailed, but it cannot be understood as a limitation on the scope of the invention patent. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several modifications and improvements can still be made, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the invention patent should be subject to the appended claims.

Claims

1. A fuel cell vehicle control method, characterized in that: The control method comprises: Obtaining the amount of hydrogen in the on-board hydrogen storage tank, determining whether the amount of hydrogen is greater than or equal to a preset hydrogen amount threshold, and if so, starting the FCS and allowing the FCS to supply power to the fuel cell vehicle; If the hydrogen amount is less than a preset hydrogen amount threshold, the fuel cell vehicle is controlled to enter a parking state, real-time light intensity is collected, and it is determined whether the real-time light intensity is greater than a preset light intensity threshold; If the real-time light intensity is greater than or equal to the preset light intensity threshold, the photovoltaic module is started to generate electricity through the photovoltaic module to drive the PEM electrolyzer to electrolyze and produce hydrogen; If the real-time light intensity is less than the preset light intensity threshold, the mode is switched to V2X mode, and the PEM electrolyzer is driven by power grid to produce hydrogen by electrolysis.

2. A fuel cell vehicle control method as claimed in claim 1, characterized in that: The control method further comprises: Obtaining the conversion efficiency, temperature coefficient and total effective light receiving area of ​​the photovoltaic module; Obtain real-time irradiance, real-time ambient temperature and solar incidence angle; Constructing a photovoltaic power generation model according to the conversion efficiency, temperature coefficient, total effective light receiving area, real-time irradiance, real-time ambient temperature and solar incident angle; The power generation power of the photovoltaic module is predicted according to the photovoltaic power generation power model.

3. A fuel cell vehicle control method as claimed in claim 2, characterized in that: The control method further comprises: Acquire weather forecast data, and acquire predicted irradiance values ​​and predicted wind speed values ​​for future time periods according to the weather forecast data; A photovoltaic module angle dynamic optimization model is constructed according to the predicted irradiance value, predicted wind speed value, real-time ambient temperature and solar incident angle.

4. A fuel cell vehicle control method as claimed in claim 3, characterized in that: The photovoltaic power generation model is expressed as P(t)=η·A·G(t)·[1+α·(T-25)]·cosθ; Among them, P(t) represents photovoltaic power generation, η represents conversion efficiency, α represents temperature coefficient, A represents total effective light receiving area, G(t) represents real-time irradiance, T represents real-time ambient temperature, and θ represents solar incidence angle.

5. A fuel cell vehicle control method as claimed in claim 4, characterized in that: The photovoltaic module angle dynamic optimization model is expressed as Among them, G(t) represents the predicted irradiance value, V(t) represents the predicted wind speed value, represents the real-time ambient temperature, Δθ(t) represents the adjustment of the solar incident angle in the previous period, and G max , T max 、V max They represent the preset maximum irradiance value, maximum ambient temperature value and maximum wind speed value respectively, λ1, λ2, λ3 represent the weight coefficients of the predicted irradiance value, predicted wind speed value and real-time ambient temperature respectively, and γ represents the inertia coefficient.

6. A fuel cell vehicle control method as claimed in claim 5, characterized in that: The control method further comprises: Obtaining the tilt angle adjustment amount Δθ' of the photovoltaic module per unit time according to the photovoltaic module angle dynamic optimization model; Obtaining a depreciation cost coefficient k of the photovoltaic module; According to the photovoltaic power generation, the tilt adjustment amount and the depreciation cost coefficient, the objective function J=P(t)-k(Δθ') is constructed. 2 .

7. A fuel cell vehicle control system, used to implement the fuel cell vehicle control method according to any one of claims 1 to 6, characterized in that: The control system comprises: Parameter acquisition module, used to obtain the amount of hydrogen in the on-board hydrogen storage tank and the real-time light intensity; a control module, configured to determine whether the amount of hydrogen is greater than or equal to a preset hydrogen amount threshold, and if so, to start the FCS and have the FCS supply power to the fuel cell vehicle; and if the amount of hydrogen is less than the preset hydrogen amount threshold, to control the fuel cell vehicle to enter a parking state, and to determine whether the real-time light intensity is greater than a preset light intensity threshold; The control module is also used to start the photovoltaic module when the real-time light intensity is greater than or equal to a preset light intensity threshold, to drive the PEM electrolyzer to electrolyze and produce hydrogen through the photovoltaic module to generate electricity, and to switch to the V2X mode when the real-time light intensity is less than the preset light intensity threshold, to drive the PEM electrolyzer to electrolyze and produce hydrogen through power supply from the grid.

8. A fuel cell vehicle control system as claimed in claim 7, characterized in that: The parameter acquisition module is also used to obtain the conversion efficiency, temperature coefficient, total effective light receiving area, real-time irradiance, real-time ambient temperature and solar incident angle of the photovoltaic module. The control system also includes: A power prediction module is used to construct a photovoltaic power generation model P(t)=η·A·G(t)·[1+α·(T-25)]·cosθ according to the conversion efficiency η, temperature coefficient α, total effective light receiving area A, real-time irradiance G(t), real-time ambient temperature T and solar incident angle θ, and predict the power generation of the photovoltaic module according to the photovoltaic power generation model.

9. A fuel cell vehicle control system as claimed in claim 8, characterized in that: The control system further comprises: Angle optimization module, used to build a dynamic optimization model for photovoltaic module angle according to the predicted irradiance value, predicted wind speed value, real-time ambient temperature and solar incident angle Among them, G(t) represents the predicted irradiance value, V(t) represents the predicted wind speed value, represents the real-time ambient temperature, Δθ(t) represents the adjustment of the solar incident angle in the previous period, and G max , T max 、V max They represent the preset maximum irradiance value, maximum ambient temperature value and maximum wind speed value respectively, λ1, λ2, λ3 represent the weight coefficients of the predicted irradiance value, predicted wind speed value and real-time ambient temperature respectively, and γ represents the inertia coefficient.

10. A fuel cell vehicle control system as claimed in claim 9, characterized in that: The control system further comprises: The objective function building module is used to build the objective function J = P(t)-k(Δθ') according to the photovoltaic power generation power P(t), the tilt adjustment amount Δθ' of the photovoltaic module per unit time, and the depreciation cost coefficient k of the photovoltaic module. 2 .

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