Integrated control system, method and apparatus for real-time control-based hydrogen filling

Abstract

An object of the present invention is to improve the efficiency of the hydrogen filling / supply process while safely filling / supplying hydrogen fuel, and to improve the speed and real-time nature of the hydrogen filling / supply process. [Solution] The hydrogen fueling control method for hydrogen fueling mobility of the present invention is an integrated control method for hydrogen filling, and is characterized by including the steps of acquiring measurement values ​​of the current state, determining whether a second control command for at least one of the hydrogen filling station and hydrogen fueled mobility is necessary in addition to a first control command executed on the dispenser side based on the measurement values ​​of the current state, and generating a second control command for at least one of the hydrogen filling station and hydrogen fueled mobility based on the determination result.

Description

[Technical Field]

[0001] The present invention relates to a control technology for hydrogen fueling / supply for hydrogen fueled mobility, and more particularly to a hydrogen fueling process that improves the efficiency of hydrogen fueling / supply and increases the speed and real-timeness of hydrogen fueling / supply, a control technique for the process, and a protocol for the control technique. [Background technology]

[0002] A hydrogen vehicle, hydrogen electric vehicle, or fuel cell electric vehicle (FCEV) is a non-polluting vehicle that runs on electrical energy generated when high-pressure hydrogen stored in the vehicle meets atmospheric air. Hydrogen electric vehicles use hydrogen as an energy source and are powered by a fuel cell system that produces electricity. As is well known, hydrogen electric vehicles not only emit pure water (H2O) during the electricity generation process, but also have the function of removing ultrafine dust particles from the atmosphere while in operation, making them garnering attention as an eco-friendly form of mobility for the future. Given that hydrogen, the fuel, is limitless on Earth and the energy production process is environmentally friendly, hydrogen electric vehicles are attracting attention as a technology with potential for use across industries. Hydrogen-fueled mobility refers to a type of mobility that uses hydrogen as an energy source or hydrogen as fuel to generate electrical energy, which is then used to drive an electric motor. In addition to the hydrogen electric vehicles mentioned above, hydrogen-fueled mobility can also include aerial mobility, as well as industrial trucks, trains, ships, and aircraft that use hydrogen as fuel to generate electrical energy and use it to power them. More generally, buildings or facilities that use hydrogen as an energy source may also be included in the application field to which the technical concept of the present invention can be applied.

[0003] A hydrogen electric vehicle generates electrical energy by transferring high-pressure hydrogen stored safely in a hydrogen fuel storage tank and oxygen supplied through an air supply system to a fuel cell stack, where an electrochemical reaction occurs between the hydrogen and oxygen. The generated electrical energy is converted into kinetic energy by the drive motor to power the hydrogen electric vehicle, and the hydrogen electric vehicle has the advantage of emitting only pure water through the exhaust while in motion. Fuel cell systems power hydrogen electric vehicles in place of internal combustion engines. Fuel cells are devices that generate the electrical energy needed for driving, and are also known as "tertiary batteries." Fuel cells convert thermal energy into electrical energy using an electrochemical reaction between oxygen and hydrogen. The electrical energy generated is the result of a purely chemical reaction, and unlike fossil fuels, does not produce exhaust gases such as carbon dioxide. There are various types of fuel cell systems, such as PEMFC, SOFC, and MCFC, depending on the fuel and material. The components that generate power using fuel cells in hydrogen electric vehicles include a fuel cell stack, hydrogen supply system, air supply system, and thermal management system. The fuel cell stack requires the assistance of operating devices to efficiently generate electrical energy. Among these, the hydrogen supply system converts hydrogen safely stored in the hydrogen fuel storage tank from a high-pressure state to a low-pressure state and transfers it to the fuel cell stack. It also increases the efficiency of hydrogen supply through a recirculation line. A thermal management system is a device that maintains a constant temperature in a fuel cell stack by dissipating heat generated during electrochemical reactions and circulating coolant. The thermal management system can affect the output and lifespan of a fuel cell stack.

[0004] The concept of a hydrogen fueled car, which is not a hydrogen electric car, is also a vehicle that uses hydrogen as fuel, but in a hydrogen fueled car, the electric motor is driven by the heat generated by directly burning hydrogen in the engine. The method of filling / supplying hydrogen for a hydrogen fueled car is not much different from that for a hydrogen electric car. The ultimate goal of the control technique for filling / supplying hydrogen to vehicles that use hydrogen as fuel is to control the temperature (T) and pressure (P) of the compressed hydrogen storage system (CHSS) on the fuel cell side so that it operates within the limit temperature / pressure conditions for safety. The hydrogen filling / supply process, control techniques, and protocols for conventional hydrogen electric vehicles were established at a time when wired / wireless communication technologies and control computing techniques were not yet mature, and therefore are unable to fully reflect the recent advances in information and communications technology (ICT). Therefore, conventional hydrogen filling / supply technologies for hydrogen electric vehicles are inefficient, slow, and not suitable for large-volume hydrogen filling. Summary of the Invention [Problem to be solved by the invention]

[0005] The object of the present invention to solve the above problems is to safely fill / supply hydrogen fuel while improving the efficiency of the hydrogen filling / supply process and improving the speed and real-timeness of the hydrogen filling / supply process.

[0006] The object of the present invention is to reduce the filling time within the usable range by operating at the optimum maximum filling speed that satisfies the conditions below the safety limit.

[0007] It is an object of the present invention to increase the operational efficiency of hydrogen filling stations by actively adjusting pre-cooling requirements and provision to optimize the station's cooling load.

[0008] An object of the present invention is to provide a control technique that can be widely and scalably applied to various mobility areas by updating the logic itself through a certain learning and training process when applied to a new device.

[0009] The objective of the present invention is to install a cooling system in the storage tank itself to increase the filling speed, while at the same time improving the safety of hydrogen fuelled mobility by being able to actively deal with storage tank overheating. [Means for solving the problem]

[0010] To achieve the above object, the hydrogen fueling control method for hydrogen-fueled mobility of the present invention is an integrated control method for hydrogen fueling, characterized by including the steps of: acquiring current state measurements; determining, based on the current state measurements, whether a second control command for at least one of the hydrogen filling station and hydrogen-fueled mobility is necessary in addition to a first control command executed on the dispenser side; and generating a second control command for at least one of the hydrogen filling station and hydrogen-fueled mobility based on the determination result.

[0011] The second control instructions for the hydrogen filling station include instructions to adjust a target value for a pre-cooling temperature of the hydrogen filling station.

[0012] The second control command for hydrogen fueled mobility includes a cooling command for a compressed hydrogen storage system (CHSS) on the hydrogen fueled mobility side.

[0013] The integrated control method for hydrogen filling of the present invention further includes the step of evaluating whether the current state measurements satisfy the constraints.

[0014] The constraint is that the temperature and pressure of the compressed hydrogen storage system (CHSS) on the hydrogen fuel mobility side do not exceed the limit temperature and limit pressure, respectively.

[0015] The integrated control method for hydrogen filling of the present invention further includes the step of generating a first control command to be executed at the dispenser side based on the current state measurement.

[0016] The first control command includes a control command for a pressure ramp rate (PRR) for hydrogen filling.

[0017] The step of generating the first control command generates the first control command based on a simulation result for the measured value of the current state and actual field data.

[0018] The step of generating the first control command generates the first control command using a model predictive control technique based on measurements of the current state.

[0019] The step of generating the first control command generates the first control command based on the output of an artificial neural network that receives the current state measurement as input.

[0020] The hydrogen fueling control device for hydrogen-fueled mobility of the present invention is an integrated control device for hydrogen filling, and includes a processor and a memory for storing at least one instruction. The processor executes at least one instruction to obtain a measurement value of a current state, determines whether a second control command for at least one of the hydrogen filling station and the hydrogen-fueled mobility is necessary in addition to a first control command executed by the dispenser based on the measurement value of the current state, and generates the second control command for at least one of the hydrogen filling station and the hydrogen-fueled mobility according to the determination result.

[0021] The processor evaluates whether the measurements of the current state satisfy the constraints.

[0022] The processor generates first control instructions to be executed at the dispenser based on the current state measurements.

[0023] The processor generates a first control command based on the simulation results and actual field data for the current state measurements.

[0024] The processor generates the first control command using a model predictive control technique based on measurements of the current state.

[0025] The processor generates first control instructions based on the output of the artificial neural network, which receives as input the measurements of the current conditions. [Effects of the Invention]

[0026] According to the present invention, the filling speed is controlled in real time using real-time temperature data of the storage tank, and it is designed to operate at the highest filling speed that satisfies the conditions below the safety limit, thereby reducing the filling time within the usable range.

[0027] According to the present invention, the pre-cooling demand and supply can be actively adjusted to optimize the cooling load of the hydrogen filling station, thereby increasing the operational efficiency of the hydrogen filling station.

[0028] According to the present invention, a learnable filling logic based on an artificial neural network is applied to a new device, and the logic itself is updated through a certain learning and training process, which allows for a wide range of applications in various mobility fields.

[0029] The present invention allows for the installation of a cooling system within the storage tank itself to increase filling speeds while also actively combating overheating in the storage tank, improving the safety of hydrogen-fueled mobility. [Brief explanation of the drawings]

[0030] [Figure 1] FIG. 1 is a conceptual diagram illustrating an example of a hydrogen filling process for a hydrogen electric vehicle (FCEV) of the present invention. [Figure 2] FIG. 1 is a conceptual diagram illustrating an example of a logical process for hydrogen filling for a hydrogen electric vehicle (FCEV) of the present invention. [Figure 3] FIG. 2 is a conceptual diagram illustrating an example of state changes that occur during the hydrogen filling process for a hydrogen electric vehicle (FCEV) of the present invention. [Figure 4] FIG. 1 is a conceptual diagram illustrating the concept of model predictive control for hydrogen filling control for a hydrogen electric vehicle (FCEV) of the present invention. [Figure 5]1 is an operational flowchart illustrating a model predictive control-based hydrogen filling control method according to the present invention. [Figure 6] FIG. 1 is a conceptual diagram illustrating the concept of an artificial neural network for hydrogen filling control for a hydrogen electric vehicle (FCEV) of the present invention. [Figure 7] 1 is a conceptual diagram illustrating a hydrogen filling control process for a hydrogen electric vehicle (FCEV) using an artificial neural network and an intelligent meta-system (IMS) of the present invention. [Figure 8] 2 is an operational flowchart illustrating an artificial neural network-based hydrogen filling control method according to the present invention. [Figure 9] 1 is an operational flowchart illustrating a training process of an artificial neural network for artificial neural network-model predictive control-based hydrogen filling control according to the present invention. [Figure 10] FIG. 10 is a conceptual diagram illustrating in detail a portion of the process of FIG. 9. [Figure 11] FIG. 10 is a conceptual diagram illustrating in detail a portion of the process of FIG. 9. [Figure 12] FIG. 2 is a conceptual diagram illustrating the hydrogen filling control process based on the artificial neural network-model predictive control of the present invention. [Figure 13] 1 is a conceptual diagram illustrating an artificial neural network-based integrated control model for controlling the hydrogen filling process of the present invention. [Figure 14] 14 is a diagram illustrating an event-based control process for the integrated control model of FIG. 13. [Figure 15] 4 is an operational flow chart illustrating the integrated control method for hydrogen filling of the present invention. [Figure 16] FIG. 16 is a conceptual diagram illustrating an example of a generalized hydrogen filling control device, hydrogen filling control system, or computing system capable of performing at least a portion of the processes of FIGS. 1 to 15. DETAILED DESCRIPTION OF THE INVENTION

[0031] The present invention can be modified in various ways and can have various embodiments, and specific embodiments will be illustrated in the drawings and described in detail in the detailed description. However, this is not intended to limit the present invention to the specific embodiments, and it should be understood that the present invention includes all modifications, equivalents, and alternatives that fall within the spirit and technical scope of the present invention. In the description of each drawing, similar reference numerals are used to refer to similar components. Terms such as "first," "second," "A," and "B" may be used to describe various components, but the components should not be limited by the terms. Terms are used only to distinguish one component from another. For example, a first component may be designated as a second component, and similarly, a second component may be designated as a first component, without departing from the scope of the present invention. The term "and / or" includes a combination of multiple associated listed items or any multiple associated listed items. In the examples of this application, "at least one of A and B" may mean "at least one of A or B" or "at least one of a combination of one or more of A and B." Also, in the examples of this application, "one or more of A and B" may mean "one or more of A or B" or "one or more of a combination of one or more of A and B." When a component is said to be "coupled" or "connected" to another component, it should be understood that it may be directly coupled or connected to the other component, but that there may be other components in between. Conversely, when a component is said to be "directly coupled" or "directly connected" to another component, it should be understood that there are no other components in between. The terms used in this application are merely used to describe specific embodiments and are not intended to limit the present invention. The singular expressions include the plural expressions unless the context clearly dictates otherwise. In this application, the terms "comprise" or "have" are intended to specify the presence of features, numbers, steps, operations, components, parts, or combinations thereof described in the specification, and should be understood not to preclude the possibility of the presence or addition of one or more other features, numbers, steps, operations, components, parts, or combinations thereof. Unless otherwise defined, all terms used herein, including technical or scientific terms, have the same meaning as commonly understood by a person of ordinary skill in the art to which this invention pertains. Terms as defined in commonly used dictionaries should be interpreted to have a meaning consistent with the meaning they have in the context of the relevant art, and should not be interpreted in an idealized or overly formal sense unless expressly defined in this application.

[0032] Some terms used in this specification are defined as follows:

[0033] A hydrogen vehicle, hydrogen electric vehicle, or fuel cell electric vehicle (FCEV) is a non-polluting vehicle that runs on electrical energy generated when high-pressure hydrogen stored in the vehicle meets atmospheric air.

[0034] A compressed hydrogen storage system (CHSS) is a device that is part of a vehicle's fuel cell and compresses and stores hydrogen.

[0035] A pressure relief device (PRD) is a device placed in a CHSS that can isolate stored hydrogen from the rest of the fuel system and the environment, or conversely, can release hydrogen to the outside.

[0036] The hydrogen fueling process refers to the process of transferring high-pressure hydrogen from a hydrogen filling station and storing it in a fuel cell.

[0037] Pressure Ramp Rate (PRR) is expressed in MPa / min and refers to the rate at which the pressure of the CHSS increases.

[0038] Average Pressure Ramp Rate (APRR) refers to the average value of the pressure ramp rate from the start to the end of hydrogen fueling.

[0039] Pre-cooling refers to the process of cooling hydrogen at a hydrogen filling station before filling it.

[0040] The dispenser is the component that delivers pre-cooled hydrogen to the CHSS.

[0041] Nozzle refers to a device that is connected to the hydrogen dispensing system of a hydrogen filling station and that connects to a receptacle of a hydrogen electric vehicle to allow delivery of hydrogen fuel.

[0042] However, even if a technology was publicly known prior to the filing date of this application, it may be included as part of the present invention, as necessary, and such technology will be described herein to the extent that it does not obscure the spirit of the present invention. However, in describing the spirit of the present invention, detailed descriptions of technology that was publicly known prior to the filing date and would be obvious to those skilled in the art will be omitted because such detailed descriptions may obscure the spirit of the present invention. For example, technology that uses a thermodynamic model for hydrogen filling control, technology that applies a model predictive control technique for generalized dynamic control, and technology that configures and controls an artificial neural network for training and inference of the artificial neural network may utilize technology that was publicly known prior to the filing of this application, and at least some of these publicly known technologies may be applied as elemental technologies necessary to implement the present invention. However, the spirit of the present invention is not intended to claim rights to such publicly known technology, and the content of such publicly known technology may be included as part of the present invention to the extent that it does not deviate from the spirit of the present invention.

[0043] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings.

[0044] FIG. 1 is a conceptual diagram illustrating an example of a hydrogen filling process for a hydrogen electric vehicle (FCEV) to which an embodiment of the present invention is applied. As shown in Figure 1, pre-cooled hydrogen gas from a hydrogen filling station (200) is supplied to a hydrogen fuel cell electric vehicle (FCEV) (300) via a dispenser 100. The hydrogen filling process can be described by parameters including the pressure rise rate (PRR) and / or the average pressure rise rate (APRR).

[0045] Although FIG. 1 illustrates an embodiment related to a hydrogen fueled electric vehicle (FCEV), it will be apparent to those skilled in the art that the concepts of the present invention may be applied to various types of hydrogen fueled mobility.

[0046] Hydrogen-fueled mobility refers to a type of mobility that uses hydrogen as an energy source or hydrogen as fuel to generate electrical energy, which is then used to drive an electric motor. In addition to hydrogen electric vehicles, hydrogen-fueled mobility can also include aerial mobility, as well as industrial trucks, trains, ships, and aircraft that use hydrogen as fuel to generate electrical energy and use it to power them.

[0047] The hydrogen filling process of the present invention may be applied not only to hydrogen-fueled mobility but also to hydrogen-powered buildings or facilities.

[0048] Also, although the embodiment of FIG. 1 illustrates an embodiment related to hydrogen gas, the concept of the present invention may also be applied to liquefied hydrogen.

[0049] For the sake of convenience, the present invention will be described below using a hydrogen electric vehicle (FCEV) and hydrogen gas as the main examples. However, the scope of the present application should not be construed as being limited by these examples.

[0050] Generally, hydrogen storage systems attached to vehicles can be broadly divided into high-pressure hydrogen storage tanks, pressure control devices, high-pressure piping, and external frames. High-pressure hydrogen storage tanks have been developed and commercialized with capacities of tens to hundreds of liters, and for vehicles, small and lightweight storage tanks are connected in parallel to achieve high capacity.

[0051] High-pressure hydrogen storage tanks are commonly known as compressed hydrogen storage systems (CHSS) 310, and for the purposes of this description, the term "storage tank" refers to a CHSS 310.

[0052] In a typical hydrogen storage system, the storage of hydrogen is controlled by using a boss unit that allows hydrogen gas to enter and exit the storage tank 310. Since hydrogen cannot be injected and used simultaneously, a valve, a pressure reducing mechanism, and various measurement sensors are attached to one boss unit to control the storage of hydrogen.

[0053] The interface between the hydrogen filling station 200 and the vehicle 300 is handled by the dispenser 100, which combines information from the storage tank 310 of the vehicle 300 and fuel supply information from the filling station 200 to control the target pressure, injection rate, etc. The control logic currently used complies with the SAE J2601 (2020-05) standard.

[0054] In the prior art, there are a communication method and a non-communication method for transmitting information from the vehicle 300 to the dispenser 100. Even when communication is used, in the prior art, the temperature and pressure values ​​of the storage tank 310 of the vehicle 300 are simply transmitted unidirectionally to the dispenser 100, and the dispenser 100 does not actively use the information, but simply uses it as a safety standard such as an emergency stop at the limit temperature and pressure.

[0055] All filling logic for safe and fast filling is managed by the dispenser 100, and the storage tank 310 has only minimal safety control devices, such as automatic release of hydrogen through a pressure relief device (PRD) 320 in the event of overheating, without any active safety control methods.

[0056] 3, the filling station 200 includes a high-pressure hydrogen storage unit 220 and a pre-cooler 210 to cope with the phenomenon that the temperature of hydrogen gas rises during hydrogen filling. The pre-cooler 210 pre-cools the hydrogen gas to a low temperature, and supplies the hydrogen gas to the hydrogen electric vehicle 300 via the dispenser 100.

[0057] In one embodiment of the present invention, a basic configuration similar to that of the prior art is used, but the filling control logic 110 inside the dispenser 100 actively controls the hydrogen fueling process by utilizing status information such as temperature and pressure received from the vehicle 300 and the filling station 200, and filling status information such as the filling rate (SOC, State of Charge) of the CHSS 310.

[0058] According to one embodiment of the present invention, the filling speed is controlled in real time using real-time temperature data of the storage tank 310, and is designed to operate at the highest filling speed that satisfies the conditions below the safety limit, thereby reducing the filling time within the usable range.

[0059] The filling protocol of the prior art has excessive boundary conditions set for safety, which causes the temperature of most of the storage tanks 310 to be measured at around 40 to 50°C at the time of completion of filling, resulting in the problem of excessive pre-cooling before supply.

[0060] According to one embodiment of the present invention, the pre-cooling requirements and provision can be actively adjusted to optimize the cooling load of the hydrogen filling station 200 and increase the operational efficiency of the hydrogen filling station 200.

[0061] Conventional protocols, which are designed around lightweight hydrogen electric vehicles, have the problem that when new mobility is refueled, all variables must be reconfigured to reflect the standard.

[0062] According to one embodiment of the present invention, a learnable filling logic based on an artificial neural network is applied to a new device, and when applied to the new device, the logic itself is updated through a certain learning and training process, which allows for a wide range of applications in various mobility fields.

[0063] The only measure to prevent overheating of the storage tank 310 of the conventional hydrogen electric vehicle 300 is to release gas through the PRD 320 when the storage tank 310 is overheated above a certain temperature.

[0064] According to one embodiment of the present invention, a cooling system is installed in the storage tank 310 itself to increase the filling speed and also to actively deal with overheating of the storage tank 310, thereby improving the safety of the hydrogen electric vehicle 300.

[0065] According to one embodiment of the present invention, it is possible to safely fill / supply hydrogen fuel while improving the efficiency of the hydrogen filling / supply process, thereby improving the speed and real-timeness of the hydrogen filling / supply process.

[0066] According to one embodiment of the present invention, a hydrogen filling control technique that ensures real-time performance based on model predictive control can be provided.

[0067] According to an embodiment of the present invention, a control technique that improves the accuracy of hydrogen filling result prediction based on an artificial neural network model can be provided. The accuracy of the prediction result can be improved by reflecting real-time measurements in an artificial neural network model that uses actual filling data along with theoretical simulation results.

[0068] According to one embodiment of the present invention, the efficiency of hydrogen filling control can be improved by using an intelligent meta-system (IMS) to integrate and manage actually measured data and state information predicted from a model.

[0069] FIG. 2 is a conceptual diagram illustrating an example of a logical process for hydrogen filling for a hydrogen electric vehicle (FCEV) to which an embodiment of the present invention is applied.

[0070] As mentioned above, in the conventional hydrogen filling process, the dispenser 100 is responsible for control between the vehicle 300 and the hydrogen filling station 200, and the dispenser 100 is equipped with a protocol that is a method for injecting hydrogen into the vehicle 300 in accordance with established regulations, and this protocol is used to oversee the control.

[0071] The protocol installed in the dispenser 100 is based on the international standard SAE-J2601 (2020-05), which is equally applicable to the embodiments of the present invention within the scope consistent with the objectives of the present invention.

[0072] Regarding minimum safety requirements, simulations are carried out through thermodynamic modeling for various situations, and the parameters derived through this are used to implement a table-based single injection method and an MC-formula-based partial real-time correction method. Minimum safety requirements include upper limits for temperature and pressure conditions for CHSS310 and guidelines for State of Charge (SOC).

[0073] The simulation can be performed through thermodynamic modeling using boundary conditions including best and worst cases.

[0074] Such a configuration can be applied to the configuration of the embodiment of the present invention within the scope consistent with the object of the present invention.

[0075] 1 and 2, the following problems have been found in the prior art in which the state value is not actively controlled by the dispenser 100. The following problems also appear in the prior art that relies on simulations using a simple thermodynamic model.

[0076] Because the injection rate is determined in advance assuming the worst-case boundary conditions (excessive boundary conditions), unnecessary pre-cooling occurs, resulting in a decrease in the overall filling rate. In this case, in conventional technology, the injection rate is simply determined by the average pressure ramp rate (APRR), which can hinder active response depending on the situation. Unnecessary pre-cooling can lead to excessive energy costs and operating costs.

[0077] Since it depends on the results of simulation, there are limitations on the capacity and shape of the applicable storage tank 310, and in the case of a new system, additional resources are required for new development and application, which limits the scope of application.

[0078] Thermodynamic models require a lot of time to derive the calculation results of mathematical formulas, and their application is limited when there are no pre-calculated variables because they indirectly utilize variables derived through the model. They also have problems such as a lack of flexibility in fine-tuning the method itself.

[0079] The conventional table-based method is extremely inefficient because it does not utilize the temperature of pre-cooled hydrogen provided at the filling station 200 or the temperature of the storage tank 310 measured in the vehicle 300, and has the problem of being difficult to flexibly respond to changes in surrounding conditions.

[0080] The conventional MC-Formula-based method corrects the pre-cooling temperature in real time, but the calculation and application method is complicated and there are limitations on the objects that can be applied, making it difficult to expand.

[0081] The protocol was developed with the primary goal of completing the safe filling, and there is no alternative that can actively control unexpected situations such as excessive pre-cooling or overheating of the storage tank 310. As a result, problems such as increased operating costs due to overcooling and filling delays due to overheating have occurred.

[0082] The present invention has been developed to solve the problems of the prior art, and is characterized by its attempt to actively control state variables by reducing the dependency on simulation and reflecting real-time measurement data.

[0083] FIG. 3 is a conceptual diagram illustrating an example of state changes that occur during a hydrogen filling process for a hydrogen electric vehicle (FCEV) to which an embodiment of the present invention is applied. As shown in FIG. 3, when hydrogen is injected into the storage tank 310, the internal temperature rises due to the heat of compression, thereby increasing the temperature of the hydrogen gas inside the storage tank 310.

[0084] Temperature control during the hydrogen filling process is performed by supplying pre-cooled hydrogen gas and controlling the internal temperature of the storage tank 310 to be 85° C. or less at the time of final filling completion.

[0085] The storage tank 310 is constructed so that the carbon fiber surrounding the dome and body of the storage tank 310 has low heat transfer efficiency in order to block heat exchange between the outside atmosphere and the hydrogen gas stored inside while the vehicle is in motion.

[0086] When the temperature of the hydrogen gas inside the storage tank 310 rises during the filling process, the temperature rise on the surface of the storage tank 310 is negligible compared to the internal temperature rise until the filling is complete due to the low heat transfer characteristics of the storage tank 310.

[0087] This characteristic can mitigate the rapid temperature rise inside the storage tank 310 that occurs during filling, but since it blocks heat exchange with the outside air, a separate temperature control measure is required. However, the prior art does not include any separate cooling means other than the supply of pre-cooled hydrogen gas from the filling station 200.

[0088] The time required for full hydrogen filling is managed by pre-cooling the storage tank at the hydrogen filling station 200 and controlling the hydrogen injection rate to maintain the storage tank at a temperature below 85°C, which is the upper limit for temperature management. However, there are currently no separate temperature management measures for the storage tank 310 of the vehicle 300. Therefore, especially in summer when the outside air temperature is high, it is difficult to control the temperature of the storage tank 310 at the filling station 200, causing problems such as filling delays.

[0089] In an embodiment of the present invention, the characteristic curve of Figure 3 is used as a basic model, but unlike conventional technology, real-time data based on variables that appear in the actual surrounding environment (outside air temperature, air pressure, weather conditions, etc.) is taken into consideration to optimize the operating load of the filling station 200 in the Phase I pre-cooling stage and the filling rate (pressure rise rate, PRR) control that occurs during the filling process in Phases II to IV, thereby deriving optimal control conditions that are suited to the actual environment.

[0090] FIG. 4 is a conceptual diagram illustrating the concept of model predictive control for hydrogen filling control for a hydrogen electric vehicle (FCEV) according to one embodiment of the present invention.

[0091] In an embodiment of the present invention, while the accuracy of the hydrogen filling model is ensured at a considerable level, future filling results are predicted from the hydrogen filling model and current measurement values, and the pressure increase rate (PRR) can be controlled in real time based on the predicted value and the filling value so that specific variables, such as the hydrogen gas temperature Tgas or hydrogen gas pressure Pgas of the CHSS 310, reach the optimal filling target without violating constraints.

[0092] In an embodiment of the present invention, a model predictive control is used to calculate future output values ​​based on current measurements and model predicted values, and an operation parameter / variable is adjusted so that the predicted future response moves to a setpoint (or target) in an optimal manner.

[0093] As shown in Figure 4, n model predictions can be derived at the current time i. These n model-based predictions form the prediction horizon.

[0094] Each model-based prediction, or prediction horizon, corresponds to a control horizon, i.e., the n control commands / control actions required to make the n model predictions can form the control horizon.

[0095] In practice, the first (i+1) control action of the n model predictions and control actions derived at the current time i can be transmitted to the system. As time passes, new n model predictions and control actions are derived again at the current time i+1, which form new prediction horizons and control horizons, respectively.

[0096] This technique of controlling a system while expanding / moving the horizon is called model predictive control, and in an embodiment of the present invention, model predictive control-based control can be performed using measured and predicted values ​​for state information (state values) including the temperature and pressure of hydrogen gas in CHSS 310.

[0097] FIG. 5 is an operational flowchart illustrating a method for controlling hydrogen filling based on model predictive control according to an embodiment of the present invention.

[0098] A hydrogen fueling method for a hydrogen electric vehicle (FCEV) according to one embodiment of the present invention is a model predictive control-based hydrogen fueling method, and includes a step of acquiring a current state measurement value (S510), a step of predicting or acquiring a next state value using an artificial neural network model (artificial neural network - model predictive control technique) based on the current state measurement value (S530), and a step of generating a control command for hydrogen fueling based on a comparison result between the current state measurement value and the next state value (S520).

[0099] Current state measurements may include measurements of the temperature and pressure of the hydrogen gas inside the CHSS 310 .

[0100] The hydrogen filling method according to an embodiment of the present invention may further include the step of evaluating whether the measured value of the current state and the next state value satisfy constraints (S540).

[0101] The constraint may be that the temperature and pressure of the compressed hydrogen storage system (CHSS) of the hydrogen electric vehicle (FCEV) do not exceed a limit temperature and limit pressure, respectively.

[0102] The step of generating a control command (S520) may include generating a control command for a pressure ramp rate (PRR) for hydrogen filling.

[0103] The step of predicting or obtaining the next state value (S530) may include predicting a plurality of next state values ​​forming a prediction horizon according to the model.

[0104] The step of generating control commands (S520) may generate a plurality of control commands that form a control horizon corresponding to the prediction horizon so that the process of the future response reaching the set point is optimized based on the comparison results between the plurality of next state values ​​and the set point.

[0105] In the step of predicting or obtaining the next state value (S530) and the step of generating the control command (S520), a plurality of control commands can be obtained that form a control horizon corresponding to a prediction horizon including a plurality of next states so that the process of the future response predicted by the artificial neural network model reaching a set point can be optimized using the model predictive control technique.

[0106] The method may further include updating variables / parameters for a cost function and filling control based on the current state and next state values ​​(S550).

[0107] FIG. 6 is a conceptual diagram illustrating an artificial neural network concept for hydrogen filling control for a hydrogen electric vehicle (FCEV) according to one embodiment of the present invention. As shown in Figure 6, the input layer receives the measured values ​​of the current state.

[0108] At this time, the ambient temperature Tamb, the pre-cooled gas temperature Tpre, and the pre-cooled gas pressure Tpre may be measured at a nozzle of the dispenser 100 or the filling station 200. The hydrogen gas temperature Tgas and the hydrogen gas pressure Pgas are values ​​measured on the CHSS 310 side of the hydrogen electric vehicle 300, and the actual measured values ​​can be input to the input layer.

[0109] In the training process of an artificial neural network, the actual current measurement value is transmitted to the input layer, and the next measurement value is transmitted to the output layer, and these can be used in the learning process of the artificial neural network. At this time, the learning process of the artificial neural network can be a process of learning a function that can predict the next measurement value of the output layer based on a combination of input measurements. The correlation between the data input to the input layer and the data provided to the output layer is learned, and through this, predictions can be made using theoretical results as well as real dynamic fueling data.

[0110] In the inference or output process using an artificial neural network, actual on-site measurements are transmitted to the input layer, and a predicted value for the next measurement can be obtained as an output from the operation of the artificial neural network.

[0111] The learning process of the artificial neural network used in the embodiments of the present invention may be either shallow learning or deep learning, and the artificial neural network may be selected from known neural networks that meet the objectives of the present invention.

[0112] The values ​​input through the input layer are transmitted to the output layer after undergoing weight-based operations in the hidden layer. The state values ​​(predicted values ​​for the next state) output by the output layer may be used to calculate a state of charge variable, such as the fraction of charge (SOC), using at least a portion of a thermodynamic model.

[0113] In the present embodiment, hybrid control combining a theoretical simulation model and an artificial neural network is also possible, so that desired results can be achieved even through learning using a small amount of data, and performance that meets the objectives of the present invention can be derived even through a lightweight artificial neural network.

[0114] The real-time pressure rise rate (PRR) or mass flow rate of compressed hydrogen (kg / s) m derived from the feedback control process can influence the weights or parameters of the hidden layers of the artificial neural network.

[0115] The artificial neural network-based hydrogen filling method of the present invention can improve the accuracy of filling result predictions through models. By utilizing actual filling data along with theoretical simulation results and reflecting real-time measurements, the accuracy of prediction results can be further improved.

[0116] While conventional control protocols calculate and predict results through simulations tailored to individual situations, the present invention differs in that it utilizes a process of improving accuracy through repeated training for various situations. Due to this difference, in the embodiment of the present invention, as various theoretical values ​​and empirical results are added, the accuracy gradually improves through updates. Even if a new filling process using a new storage tank 310 configuration or a change in flow rate is introduced, the model can be updated by adding actual data and training, so that the model can be widely applied to various mobility fields.

[0117] FIG. 7 is a conceptual diagram illustrating a hydrogen filling control process for a hydrogen fuel cell electric vehicle (FCEV) using an artificial neural network and an integrated meta system (IMS) according to one embodiment of the present invention. As shown in Figure 7, the Intelligent Meta System (IMS) 400 provides the necessary information (gas temperature and pressure measured by the CHSS 310 or predicted by the model) for the artificial neural network and model predictive control, and manages distributed roles such as adjusting the pre-cooling temperature according to real-time environmental changes, thereby enabling efficient filling speed control.

[0118] The filling station 200 can communicate the pre-cooled gas measurements (conditions) to the IMS 400 . The hydrogen electric vehicle 300 can transmit the measurements (conditions) of the CHSS 310 to the IMS 400 .

[0119] Depending on the embodiment of the present invention, the IMS 400 may be disposed on the dispenser 100 side or may be embodied as an independent control device. The IMS 400 may transmit control commands for filling control to the filling station 200 and the hydrogen electric vehicle 300 side, and such a configuration will be described later through the embodiments of Figures 13 to 16.

[0120] The IMS 400 can communicate with the artificial neural network 120 to set control variables as needed to control the artificial neural network 120 to achieve optimal filling results. The next target pressure Ptarget can be derived from the predicted SOC from the artificial neural network 120, and the control parameters (PRR or m) for this can be set. At this time, a cost function can be used to adjust the control parameters so that the state values ​​satisfy the constraints in all situations.

[0121] The target pressure Ptarget can be derived to achieve the following control target, for example, based on the case where the SOC is 95% under conditions of 15° C. and NWP (Normal Working Pressure).

[0122] Constraints addressed in the cost function may include, for example, Tgas<85° C., Pgas<87.5 MPa, and SOC<100%.

[0123] The intelligent meta-system 400 may be subordinate to the dispenser 100 or may be located independently of the dispenser 100 . The intelligent meta-system 400 may be centralized in one device or distributed across multiple hardware devices, including the dispenser 100 and / or filling station 200 . The intelligent meta-system 400 may be implemented in the form of a cloud server.

[0124] Although the illustrated embodiment shows the artificial neural network 120 as being subordinate to the dispenser 100, in other embodiments of the present invention, the artificial neural network 120 may be implemented in a location independent of the dispenser 100. For example, the artificial neural network 120 may be installed and trained in a cloud system and used to control hydrogen filling via the dispenser 100 via wired / wireless communication.

[0125] Alternatively, a separate artificial neural network may be arranged and trained in the cloud system, and all or some of the parameters of the trained artificial neural network may be transferred to the local artificial neural network 120 arranged in the dispenser 100 and used for hydrogen filling control. In this case, parameters may be shared between the artificial neural network trained in the cloud and the local artificial neural network 120 using transfer learning or federated learning.

[0126] FIG. 8 is an operational flowchart illustrating an artificial neural network-based hydrogen filling control method according to one embodiment of the present invention.

[0127] A hydrogen fueling method for a hydrogen electric vehicle (FCEV) according to one embodiment of the present invention is an artificial neural network-based hydrogen fueling method, and includes a step of acquiring a current state measurement value (S610), a step of generating a filling control command for hydrogen filling based on the output of an artificial neural network that inputs the current state measurement value (S620), a step of acquiring a next state value based on the filling control command (S630), and a step of evaluating whether a response resulting from the execution of the filling control command satisfies a constraint condition (S650).

[0128] In the step of generating a filling control command (S620), the filling control command can be generated based on the output of an artificial neural network that has learned the function of generating a filling control command so that each future response after the measurement value of the current state reaches a set point while satisfying the constraints.

[0129] In the step of generating a filling control command (S620), a plurality of control commands corresponding to a plurality of next state values ​​may be generated based on the output of an artificial neural network that has learned the function of predicting a plurality of next state values ​​that minimizes a cost function for the filling state.

[0130] FIG. 9 is an operational flowchart illustrating a training process of an artificial neural network for artificial neural network-model predictive control-based hydrogen filling control according to one embodiment of the present invention. In Figure 9, we assume n future predictions and corresponding control commands obtained from an artificial neural network model 120 that has learned the function of acquiring the prediction horizon and control horizon based on the artificial neural network-model predictive control, in particular, to optimize the process by which the future response reaches the set point through model predictive control.

[0131] In an embodiment of the present invention, a control system can be configured based on the artificial neural network model 120, and a real-time control system based on model predictive control can be configured by ensuring the accuracy of the artificial neural network model 120.

[0132] The real-time control system predicts future filling results and compares them with actual measurements to control the filling speed / pressure increase rate / pressure increase speed. It can control within optimal values ​​by separately setting restrictions, control time intervals, sensitivity, etc. within the system logic.

[0133] While optimal control is primarily performed based on real-time data from the filling station 200 and the vehicle 300, if a specific event occurs during the operation of the system, the system can directly control the pre-cooling temperature of the pre-cooler 210 and the cooling system of the vehicle 300, thereby increasing the overall efficiency of the hydrogen filling process.

[0134] 9, the control process starts when the SOCsp specified by the customer is input (t=0, S710). For example, the current SOC may be 50% and the SOCsp may be 85%.

[0135] If the customer does not set a specific SOCsp, the SOCsp may be set to a pre-configured default value, for example, the default value may be 100%.

[0136] SOC(t) is given as a function of Tgas(t) and Pgas(t), and this process can be carried out based on a general kinetic model.

[0137] If the current SOC(t) is greater than or equal to SOCsp (S720), the customer can assume that the specific filling conditions have been met and the filling is complete, and the hydrogen filling can be stopped.If the current SOC(t) is less than SOCsp (S720), i is set to t, and a moving horizon prediction using an artificial neural network is performed (S730).

[0138] Step S730 may be performed by generating predictions based on the model predictive control illustrated in Figure 4 using an artificial neural network 120 or the like. In step S740, it may be determined whether the obtained n predictions are optimized predictions / control commands that match the intended purpose.

[0139] If the obtained n predictions are optimized predictions, a control command PRR(t) can be obtained based on the n predictions and the control command, and the PRR(t) can be applied to the dispenser 100-storage tank 310 (S750).

[0140] Thereafter, time t is increased to obtain new measured values ​​Tgas(t) and Pgas(t), which are then transferred to the input of step S720.

[0141] If the n predictions obtained in step S730 are not optimized predictions, step S730 can be performed again to obtain new n predictions and control commands.

[0142] FIG. 10 is a conceptual diagram illustrating in detail part of the process of FIG. 10, in step S730 of FIG. 9, a state prediction value (T, P) that satisfies the temperature and pressure constraints can be generated for all arbitrary i and k, where i is an index representing the current time, and k is an index corresponding to each prediction / control command forming the moving horizon.

[0143] Using the current time i (= t) as a reference, n predicted state values ​​and corresponding control commands can be derived.

[0144] FIG. 11 is a conceptual diagram illustrating in detail part of the process of FIG. 11, step S740 in FIG. 9 can be understood as a process of searching for a set of n predictions that minimizes a cost function representing the control sensitivity Θ and whether the final control target SOCsp has been reached. The control sensitivity Θ may include the rate of change of PRR or m, etc.

[0145] FIG. 12 is a conceptual diagram illustrating a hydrogen filling control process based on an artificial neural network-model predictive control according to an embodiment of the present invention. As shown in FIG. 12, the dispenser 100 may include hydrogen fueling control logic 110 and an artificial neural network model 120 .

[0146] State measurements including temperature and pressure of the CHSS 310 at the output of the hydrogen electric vehicle 300 may be provided as feedback inputs to the artificial neural network model 120 .

[0147] Condition measurements, including temperature and pressure, of the pre-cooled hydrogen gas at the output of the hydrogen filling station 200 may be provided as feedback inputs to the artificial neural network model 120 .

[0148] Referring to the processes of Figures 9 to 11 together with Figure 12, the artificial neural network model 120 transmits a predicted output to the hydrogen filling control logic 110, and the hydrogen filling control logic 110 can input a future input to the artificial neural network model 120.

[0149] The artificial neural network-model predictive control-based control process is a control technique that utilizes both simulation and actual measurement data, and performs simulation at least partially using an artificial neural network model 120 and uses the predicted results in the control process.

[0150] FIG. 13 is a conceptual diagram illustrating an artificial neural network-based integrated control model for controlling a hydrogen filling process according to one embodiment of the present invention.

[0151] The embodiment of the present invention aims to configure a real-time data-based integrated hydrogen filling control protocol, and the system is realized by utilizing various elemental technologies.

[0152] The protocol installed in the dispenser 100 utilizes pre-cooled hydrogen gas data provided from the filling station 200 and storage tank 310 data provided from the vehicle 300 as real-time input values, and can control the filling rate / pressure increase rate / pressure increase speed (PRR or m) as output according to the installed model.

[0153] When an event situation such as a change in the external environment occurs, the pre-cooling temperature of the filling station 200 and the cooling system of the vehicle 300 can be directly controlled to generally control the filling speed / pressure increase rate / pressure increase speed (PRR or m) and process efficiency.

[0154] To complement the control protocol, the pre-cooling system / pre-cooler 210 of the hydrogen filling station 200 may be independently equipped with its own cooling stabilization system.

[0155] In terms of temperature stabilization, the cooling stabilization system of the pre-cooler 210 can be independently controlled, and the control target value can be changed integrally with the protocol of the dispenser 100 .

[0156] To improve the economics of the filling station 200 and to complement the functionality of the integrated control protocol, the precooler 210 may be provided with additional functions related to temperature stabilization.

[0157] The pre-cooling temperature varies depending on the initial temperature and flow rate of the hydrogen gas supplied to the pre-cooler 210. To compensate for this, a new pre-cooler structure for stabilizing the temperature is proposed as one embodiment of the present invention.

[0158] The pre-cooler 210 according to an embodiment of the present invention may include control logic for autonomous temperature control and protocol coordination.

[0159] A forced cooling system may be installed in the storage tank 310 of the vehicle 300 to partially cool the heat of compression generated during hydrogen filling, thereby improving the filling speed, and the operation / control of the forced cooling system of the storage tank 310 may also be involved in the protocol.

[0160] In one embodiment of the present invention, temperature control may be provided to the storage tank 310 of the vehicle 300 to enhance hydrogen filling speed and complement the functionality of the integrated control protocol.

[0161] In one embodiment of the present invention, the storage tank 310 may include a system for self-cooling to increase the overall filling speed and improve the safety of the vehicle 300, and may include control logic for self-driving the system and linking it with protocols.

[0162] In one embodiment of the present invention, such integrated control not only improves the current filling efficiency but also facilitates preparation for the next filling.

[0163] FIG. 14 is a diagram illustrating an event-based control process for the integrated control model of FIG. As shown in FIG. 14, if hydrogen filling proceeds smoothly within the control standard even during normal operation, the target can be achieved by controlling only the dispenser 100 base.

[0164] In the case of T40, in which the pre-cooling temperature of the pre-cooler 210 is set to -40°C, if the pre-cooling temperature reaches the target value but the outside air temperature is higher than the set value and the temperature rise on the storage tank 310 side is greater than expected, a control signal or current status information can be transmitted to the vehicle 300 / storage tank 310 side to activate the self-cooling system of the storage tank 310.

[0165] Conversely, if the pre-cooling temperature of the pre-cooler 210 is set to -40°C, but is determined to be excessive cooling when considering the external environment and actual data, the target value of the pre-cooling temperature can be adjusted (e.g., -35°C).

[0166] If additional pre-cooling target temperature and storage tank 310 temperature control is required, control information or commands may be transmitted from the dispenser 100 to both the vehicle 300 and the filling station 200 .

[0167] According to an embodiment of the present invention, the self-refrigeration systems of the vehicle 300 and the filling station 200 may be controlled independently, or may be controlled by transmitting a signal from the dispenser 100 .

[0168] FIG. 15 is an operational flowchart illustrating an integrated control method for hydrogen filling according to one embodiment of the present invention.

[0169] A hydrogen fueling control method for a hydrogen electric vehicle (FCEV) according to one embodiment of the present invention is an integrated control method for hydrogen fueling, and includes a step of acquiring current state measurements (S810), a step of determining whether a second control command for at least one of the hydrogen filling station and the hydrogen electric vehicle is necessary in addition to a first control command executed by the dispenser based on the current state measurements (S830), and a step of generating a second control command for at least one of the hydrogen filling station and the hydrogen electric vehicle based on the determination result (S840).

[0170] The second control instructions for the hydrogen filling station may include instructions to adjust a target value for a pre-cooling temperature of the hydrogen filling station.

[0171] The second control command for the hydrogen electric vehicle may include a command to cool a compressed hydrogen storage system (CHSS) on the hydrogen electric vehicle side.

[0172] The integrated control method for hydrogen filling according to an embodiment of the present invention may further include evaluating whether the measurement value of the current state satisfies a constraint condition.

[0173] The constraint may be that the temperature and pressure of the compressed hydrogen storage system (CHSS) 310 on the hydrogen electric vehicle side do not exceed a limit temperature and limit pressure, respectively.

[0174] The integrated control method for hydrogen filling according to an embodiment of the present invention may further include generating a first control command to be executed at the dispenser side based on the measured value of the current state (S850).

[0175] Step S830 may be performed based on the result of performing step S820 of calculating the difference between the measured value and the predicted value of the current CHSS 310 state.

[0176] FIG. 16 is a conceptual diagram illustrating an example of a generalized hydrogen filling control device, hydrogen filling control system, or computing system capable of performing at least a portion of the processes of FIGS.

[0177] The hydrogen filling control device may be located on the dispenser 100 side. The hydrogen filling control system may be located in the dispenser 100, the hydrogen filling station 200, and the hydrogen electric vehicle 300, or may be located in at least some of them, and may control the operation of at least some of the dispenser 100, the hydrogen filling station 200, and the hydrogen electric vehicle 300.

[0178] The hydrogen filling control device and / or hydrogen filling control system may be embodied in the form of a computing system including a processor 1100 electronically coupled to a memory 1200 .

[0179] At least some of the processes of the model predictive control-based hydrogen filling control method, the artificial neural network-based hydrogen filling control method, and the integrated control method for hydrogen filling according to an embodiment of the present invention may be performed by the computing system 1000 of FIG.

[0180] As shown in FIG. 16, a computing system 1000 according to one embodiment of the present invention may include a processor 1100, a memory 1200, a communication interface 1300, a storage device 1400, an input interface 1500, an output interface 1600, and a bus 1700.

[0181] A computing system 1000 according to an embodiment of the present invention may include at least one processor 1100 and a memory 1200 storing instructions for instructing the at least one processor 1100 to perform at least one step. At least some steps of a method according to an embodiment of the present invention may be performed by the at least one processor 1100 loading and executing instructions from the memory 1200.

[0182] The processor 1100 may refer to a central processing unit (CPU), a graphics processing unit (GPU), or a dedicated processor on which methods according to embodiments of the present invention are performed.

[0183] Each of the memory 1200 and the storage device 1400 may be composed of at least one of a volatile storage medium and a non-volatile storage medium. For example, the memory 1200 may be composed of at least one of a read only memory (ROM) and a random access memory (RAM).

[0184] The computing system 1000 may also include a communication interface 1300 for effecting communications over a wireless network. The computing system 1000 may further include a storage device 1400, an input interface 1500, an output interface 1600, and the like. Furthermore, the components included in the computing system 1000 are connected by a bus 1700 to perform communication.

[0185] The computing system 1000 of the present invention may be, for example, a communications-enabled desktop computer, a laptop computer, a notebook computer, a smartphone, a tablet PC, a mobile phone, a smart watch, smart glasses, an e-book reader, a portable multimedia player (PMP), a portable game console, a navigation device, a digital camera, a digital multimedia broadcasting (DMB) player, a digital audio recorder, a digital audio player, a digital video recorder, a digital video player, a personal digital assistant (PDA), or the like.

[0186] The operations of the methods according to the embodiments of the present invention may be embodied as a computer-readable program or code stored in a computer-readable recording medium. The computer-readable recording medium may include any type of storage device that stores information readable by a computer system. The computer-readable recording medium may also be distributed among computer systems connected via a network, so that the computer-readable program or code may be stored and executed in a distributed manner.

[0187] Additionally, the computer-readable recording medium may include a hardware device specially configured to store and execute program instructions, such as a ROM, RAM, flash memory, etc. The program instructions may include not only machine language code, such as that produced by a compiler, but also high-level language code that can be executed by a computer using an interpreter, etc.

[0188] Some aspects of the invention have been described in the context of an apparatus, but they may also represent a corresponding method description, where a block or apparatus corresponds to a method step or feature of a method step. Similarly, aspects described in the context of a method may also be represented by a corresponding block or item or feature of a corresponding apparatus. Some or all of the method steps may be performed by (or using) a hardware device, such as, for example, a microprocessor, a programmable computer, or electronic circuitry. In some embodiments, at least one or more of the most significant method steps may be performed by such an apparatus.

[0189] In some embodiments, a programmable logic device (e.g., a field programmable gate array) may be used to perform some or all of the functions of the methods described herein. In some embodiments, a field programmable gate array may operate in conjunction with a microprocessor to perform one of the methods described herein. In general, it is preferred that the methods be performed by some hardware device.

[0190] Although the present invention has been described above with reference to preferred embodiments, it will be understood by those skilled in the art that various modifications and variations of the present invention may be made without departing from the spirit and scope of the present invention as set forth in the following claims. [Explanation of symbols]

[0191] 100 Dispensers 110 Hydrogen filling control 120 Artificial Neural Networks 200 Hydrogen filling station, filling station 210 Pre-cooler 220 High-pressure hydrogen 300 Hydrogen fueled mobility, mobility, hydrogen electric vehicles, vehicles 310 Compressed Hydrogen Storage System (CHSS), storage tank 320 Pressure Relief Device (PRD) 400 Intelligent Metasystem 1000 Computing Systems 1100 processor 1200 memory 1300 Communication Interface 1400 Storage 1500 Input User Interface 1600 Output User Interface 1700 Bus

Claims

1. A hydrogen fueling control method for hydrogen fueled mobility, comprising: obtaining measurements of the current state of a compressed hydrogen storage system (CHSS) within the hydrogen-fueled mobility; generating first control commands, including a control command for a pressure ramp rate (PRR) for hydrogen filling, to be executed by the dispenser based on the current state measurements; calculating a difference between the measured value of the current state and a predicted value of a filling result predicted from the measured value of the current state; determining whether at least one of a command to adjust a target value of a pre-cooling temperature of a hydrogen filling station and a command to cool the compressed hydrogen storage system is necessary based on a difference between the measured value of the current state and the predicted value; generating the second control command when it is determined that the second control command is necessary; The integrated control method for hydrogen filling, wherein the first control command is a command to control the pressure increase rate using data on pre-cooled hydrogen gas provided from the hydrogen filling station and data on the compressed hydrogen storage system as real-time input values.

2. 2. The integrated control method for hydrogen filling according to claim 1, further comprising a step of evaluating whether the measured value of the current state satisfies a constraint that the temperature and pressure of the compressed hydrogen storage system do not exceed a limit temperature and a limit pressure, respectively.

3. The hydrogen filling station includes a pre-cooler for independent temperature control; 2. The integrated control method for hydrogen filling according to claim 1, wherein the compressed hydrogen storage system includes a temperature management function for self-cooling.

4. 2. The integrated control method for hydrogen filling according to claim 1, wherein the generating of the first control command comprises generating the first control command based on a simulation result for the measured value of the current state and actual field data.

5. 2. The integrated control method for hydrogen filling according to claim 1, wherein the generating of the first control command comprises generating the first control command using a model predictive control technique based on the measurement value of the current state.

6. 2. The integrated control method for hydrogen filling according to claim 1, wherein the step of generating the first control command comprises generating the first control command based on the output of an artificial neural network that receives the measured value of the current state as an input.

7. 1. A hydrogen fueling control device for hydrogen fueled mobility, comprising: a processor; a memory for storing at least one instruction; The processor executes the at least one instruction to: obtaining measurements of the current state of a compressed hydrogen storage system (CHSS) within said hydrogen-fueled mobility; generating first control commands, including a control command for a pressure ramp rate (PRR) for hydrogen filling, to be executed at the dispenser based on the current state measurements; calculating a difference between the current state measurement and a predicted filling result predicted from the current state measurement; determining whether at least one or more second control commands are necessary based on a difference between the measured value of the current state and the predicted value, including a command to adjust a target value of a pre-cooling temperature of the hydrogen filling station and a command to cool the compressed hydrogen storage system; If it is determined that the second control command is necessary, generating the second control command; The integrated control device for hydrogen filling, characterized in that the first control command is a command to control the pressure increase rate using data on pre-cooled hydrogen gas provided from the hydrogen filling station and data on the compressed hydrogen storage system as real-time input values.

8. 8. The integrated control device for hydrogen filling according to claim 7, wherein the processor evaluates whether the measured value of the current state satisfies a constraint that the temperature and pressure of the compressed hydrogen storage system do not exceed a limit temperature and a limit pressure, respectively.

9. The hydrogen filling station includes a pre-cooler for independent temperature control; 8. The integrated control device for hydrogen filling according to claim 7, wherein the compressed hydrogen storage system includes a temperature control function for self-cooling.

10. 8. The integrated control device for hydrogen filling according to claim 7, wherein the processor generates the first control command based on a simulation result for the measured value of the current state and actual field data.

11. The integrated control device for hydrogen filling according to claim 7, wherein the processor generates the first control command using a model predictive control technique based on the measurement value of the current state.

12. 8. The integrated control device for hydrogen filling according to claim 7, wherein the processor generates the first control command based on the output of an artificial neural network that receives the current state measurement as an input.

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