Hydrogen fueling method optimized using predicted time cycle control of artificial neural network model and controller for same

Abstract

A method for fueling a mobility vehicle that uses hydrogen as fuel according to the present invention includes: a step for performing a first hydrogen fuel supply process controlled on the basis of a first fuel supply protocol; a step for acquiring first fuel supply data obtained from the first hydrogen fuel supply process; and a step for providing control information for a second hydrogen fuel supply process on the basis of the first fuel supply data.

Description

An optimized hydrogen fuel supply method and its control device using the prediction time cycle control of an artificial neural network model

[0001] The present invention relates to a control technology for hydrogen fueling of hydrogen fuel mobility, and more particularly, to a hydrogen fueling process that increases the efficiency of hydrogen fueling and the convenience, speed, and real-time nature of fueling, and a platform for the process, and to a hydrogen fueling method optimized using prediction time cycle control of an artificial neural network model and a control device for the same.

[0002] The material described in this section merely provides background information for the present embodiment and does not constitute prior art.

[0003] Hydrogen vehicles, or hydrogen electric vehicles, are zero-emission vehicles that run on electricity generated by combining high-pressure hydrogen stored onboard with air. They are also called fuel cell electric vehicles (FCEVs). Most FCEVs use hydrogen as their energy source, generating electricity through a fuel cell system. FCEVs not only emit only pure water (H2O) during the electricity generation process, but also have the ability to remove ultrafine dust from the atmosphere during operation, making them a promising future eco-friendly form of mobility. Given the inexhaustible availability of hydrogen as a fuel and the environmental friendliness of the energy production process, they are gaining widespread attention as a technology with potential for widespread industrial application.

[0004] Hydrogen-fueled mobility refers to mobility that uses hydrogen as an energy source or fuel to generate electricity and use it to drive an electric motor. In addition to the hydrogen-powered vehicles described above, hydrogen-fueled mobility also includes aerial mobility, industrial trucks, trains, ships, and aircraft, as well as devices that use hydrogen as fuel to generate electricity and use it to drive vehicles.

[0005] Most hydrogen fuel cell vehicles use high-pressure hydrogen safely stored in hydrogen fuel tanks and oxygen supplied through an air supply system to deliver it to the fuel cell stack, where the electrochemical reaction between the hydrogen and oxygen produces electrical energy. This electrical energy is converted into kinetic energy via the drive motor, propelling the vehicle. While in motion, hydrogen fuel cell vehicles have the advantage of emitting only pure water through the exhaust.

[0006] Meanwhile, the concept of a hydrogen fuel cell vehicle (HV)—not a hydrogen electric vehicle—also refers to a vehicle that uses hydrogen as fuel. A HV uses the heat generated by directly combusting hydrogen in an internal combustion engine (ICE) to drive an electric motor. The method of supplying hydrogen for HVs is not significantly different from that for hydrogen electric vehicles.

[0007] The control technique for supplying hydrogen to a vehicle that uses hydrogen as fuel ultimately aims to control the temperature and pressure of the compressed hydrogen storage system (CHSS) on the fuel cell side to operate under the limit temperature and limit pressure conditions for the safety of hydrogen fuel supply.

[0008] The hydrogen fueling process, control techniques, and protocols for conventional hydrogen fuel cell vehicles were developed when wired and wireless communication technologies and control computing techniques were not yet mature. Therefore, they do not adequately reflect recent advancements in information and communication technology (ICT). Therefore, conventional hydrogen fueling technologies for hydrogen fuel cell vehicles are inefficient, slow, and unsuitable for large-scale hydrogen fueling.

[0009] The purpose of the present invention to solve the above problems is to propose a hydrogen fueling process for hydrogen fueled mobility, a communication protocol for the process, and a hydrogen fueling process that can overcome the limitations and vulnerabilities of existing one-way communication in the fueling protocol and improve the safety, compatibility, efficiency, and reliability of hydrogen fueling.

[0010] Another object of the present invention is to improve the speed and real-time performance of the hydrogen fueling process and to improve the accuracy of the control and prediction process of the hydrogen fueling process by utilizing additional means such as an artificial neural network model and / or model predictive control.

[0011] Another object of the present invention is to propose a process for applying an optimized communication protocol and fueling protocol that can utilize conventional or advanced communication media to enable hydrogen fuel mobility and dispensers to effectively achieve hydrogen fueling goals.

[0012] Another object of the present invention is to propose an optimized fuel supply process according to the surrounding circumstances and conditions when supplying hydrogen fuel.

[0013] According to one embodiment of the present invention for achieving the above object, a method for fueling hydrogen in a hydrogen-fueled mobility may include the steps of performing a first hydrogen fueling process controlled based on a first fueling protocol; acquiring first fueling data obtained in the first hydrogen fueling process; and providing control information for a second hydrogen fueling process according to a first prediction cycle based on the first fueling data.

[0014] In a method for supplying hydrogen to hydrogen fuel mobility according to one embodiment of the present invention, the first prediction period can be determined by user input.

[0015] In a method for supplying hydrogen to hydrogen fuel mobility according to one embodiment of the present invention, the first prediction period may be determined based on a) reliability of a model for generating control information, b) reliability of fuel supply data obtained by communication between the mobility and a dispenser for supplying hydrogen to the mobility, c) reliability of the mobility or the dispenser, or d) stability of climatic environmental conditions.

[0016] In a method for supplying hydrogen to hydrogen fuel mobility according to one embodiment of the present invention, control information can be generated by a model prediction control (MPC) operation.

[0017] In a method for supplying hydrogen to hydrogen fuel mobility according to one embodiment of the present invention, control information can be generated by an artificial neural network (ANN) model.

[0018] According to one embodiment of the present invention, a method for supplying hydrogen to hydrogen fuel mobility may further include a step of determining a second prediction cycle for predicting a third hydrogen fueling process after a second hydrogen fueling process.

[0019] At this time, the method for supplying hydrogen to hydrogen fuel mobility according to one embodiment of the present invention may further include a step of obtaining second fuel supply data obtained in a second hydrogen fuel supply process.

[0020] At this time, the step of determining the second prediction cycle may determine the second prediction cycle based on the predicted state information in the process of generating the second fuel supply data and the control information for the second hydrogen fuel supply process.

[0021] According to one embodiment of the present invention, a method for supplying hydrogen to hydrogen fuel mobility may further include a step of determining whether a first fuel supply protocol supports a process of generating control information by model prediction control (MPC).

[0022] According to one embodiment of the present invention, a method for fueling hydrogen to a hydrogen fuel mobility may further include a step of determining whether a communication protocol associated with the first fueling protocol supports bidirectional communication between the mobility and a dispenser that fuels the mobility with hydrogen.

[0023] According to one embodiment of the present invention, a method for supplying hydrogen to a hydrogen fuel mobility may further include a step of determining whether the mobility and a dispenser supplying hydrogen to the mobility support two-way communication between the mobility and the dispenser.

[0024] According to one embodiment of the present invention, a device for controlling a process of supplying hydrogen fuel to a hydrogen-fueled mobility device includes a memory storing at least one command; and a processor executing at least one command, wherein the processor is capable of performing a first hydrogen fueling process controlled based on a first fueling protocol by the at least one command, obtaining first fueling data obtained in the first hydrogen fueling process, and providing control information for a second hydrogen fueling process according to a first prediction cycle based on the first fueling data.

[0025] In a device for controlling a process of supplying hydrogen to a hydrogen-fueled mobility according to one embodiment of the present invention, a first prediction period can be determined by a user input.

[0026] In a device for controlling a process of supplying hydrogen to a hydrogen-fueled mobility according to one embodiment of the present invention, a first prediction period may be determined based on a) reliability of a model for generating control information, b) reliability of fuel supply data obtained through communication between the mobility and a dispenser for supplying hydrogen to the mobility, c) reliability of the mobility or the dispenser, or d) stability of climatic environmental conditions.

[0027] In a device for controlling a process of supplying hydrogen to a hydrogen-fueled mobility according to one embodiment of the present invention, control information may be generated by a model prediction control (MPC) operation.

[0028] In a device for controlling a process of supplying hydrogen to a hydrogen-fueled mobility according to one embodiment of the present invention, control information may be generated by an artificial neural network (ANN) model.

[0029] In a device for controlling a process of supplying hydrogen fuel to a hydrogen-fueled mobility according to one embodiment of the present invention, the processor may determine a second prediction cycle for predicting a third hydrogen fueling process following a second hydrogen fueling process.

[0030] At this time, the processor can obtain second fuel supply data obtained during the second hydrogen fuel supply process.

[0031] At this time, the processor can determine the second prediction cycle based on the predicted state information in the process of generating the second fuel supply data and the control information for the second hydrogen fuel supply process when determining the second prediction cycle.

[0032] In a device for controlling a process of supplying hydrogen to a hydrogen-fueled mobility according to one embodiment of the present invention, a processor may determine whether a first fuel supply protocol supports a process of generating control information by model prediction control (MPC).

[0033] In a device for controlling a process of supplying hydrogen to a hydrogen-fueled mobility according to one embodiment of the present invention, the processor may determine whether a communication protocol associated with a first fuel supply protocol supports bidirectional communication between the mobility and a dispenser that supplies hydrogen to the mobility.

[0034] In a device for controlling a process of supplying hydrogen to a hydrogen-fueled mobility according to one embodiment of the present invention, a processor may determine whether the mobility and a dispenser supplying hydrogen to the mobility support two-way communication between the mobility and the dispenser.

[0035] According to one embodiment of the present invention, the limitations and vulnerabilities of existing one-way communication in the hydrogen fueling process of hydrogen fueled mobility, the communication protocol for the process, and the fueling protocol can be overcome, and the safety, compatibility, efficiency, and reliability of hydrogen fueling can be improved.

[0036] According to one embodiment of the present invention, the speed and real-time performance of the hydrogen fueling process can be improved, and the accuracy of the control and prediction process of the hydrogen fueling process can be improved by utilizing additional means such as an artificial neural network model and / or model predictive control.

[0037] According to one embodiment of the present invention, an optimized fuel supply process can be implemented according to the surrounding circumstances and conditions when supplying hydrogen fuel.

[0038] FIG. 1 is a conceptual diagram illustrating a hydrogen fuel supply and control process for hydrogen fueled mobility to which one embodiment of the present invention is applied.

[0039] FIG. 2 is a conceptual diagram illustrating an example of state changes occurring during a hydrogen fuel supply process for a hydrogen vehicle / hydrogen mobility to which one embodiment of the present invention is applied.

[0040] FIG. 3 is a conceptual diagram illustrating a platform or test platform to which a hydrogen fuel supply process according to one embodiment of the present invention is applied.

[0041] FIG. 4 is a conceptual diagram illustrating a model prediction control (MPC) process according to one embodiment of the present invention.

[0042] FIG. 5 is a conceptual diagram illustrating a platform or test platform for simulation used in a hydrogen fuel supply process according to one embodiment of the present invention.

[0043] FIG. 6 is a conceptual diagram illustrating a concept of modeling, predicting, or controlling a hydrogen fuel supply process using an artificial neural network (ANN) in a hydrogen fuel supply process according to one embodiment of the present invention.

[0044] FIG. 7 is a conceptual diagram illustrating a concept of controlling a hydrogen fuel supply process to reach a target output using model predictive control in a hydrogen fuel supply process according to one embodiment of the present invention.

[0045] FIG. 8 is a flowchart illustrating the training and inference process of an artificial neural network when an artificial neural network is used for model prediction control in a hydrogen fuel supply process according to one embodiment of the present invention.

[0046] FIG. 9 is a framework for functional blocks that perform a series of hydrogen fueling procedures that can employ a hydrogen fueling communication bidirectional process according to one embodiment of the present invention.

[0047] Figure 10 is a flowchart illustrating a hydrogen fuel supply method according to one embodiment of the present invention.

[0048] FIG. 11 is a diagram showing an example of a prediction time cycle for implementing the embodiment of FIG. 10 on a time axis.

[0049] FIG. 12 is a conceptual diagram illustrating an example of a platform on which a hydrogen fuel supply process for implementing the embodiment of FIG. 10 is performed.

[0050] FIG. 13 is a conceptual diagram illustrating an example of a generalized hydrogen fuel supply control device, hydrogen fuel supply control system, hydrogen fuel supply simulation device, hydrogen fuel supply simulation system, hydrogen fuel supply test platform, hydrogen fuel supply test system, or computing system capable of performing at least a portion of the processes of FIGS. 1 to 12.

[0051] In addition to the above purpose, other objects and features of the present invention will become apparent through the description of embodiments with reference to the attached drawings.

[0052] The present invention is susceptible to various modifications and embodiments. Specific embodiments are illustrated in the drawings and described in detail in the detailed description. However, this is not intended to limit the present invention to specific embodiments, but rather to encompass all modifications, equivalents, and alternatives falling within the spirit and technical scope of the present invention. Throughout the description of each drawing, similar reference numerals have been used to designate similar components.

[0053] Terms such as "first," "second," "A," and "B" may be used to describe various components, but these components should not be limited by these terms. These terms are used solely to distinguish one component from another. For example, without departing from the scope of the present invention, the first component could be referred to as the "second component," and similarly, the second component could also be referred to as the "first component." The term "and / or" includes any combination of multiple related items listed or any one of multiple related items listed.

[0054] In the embodiments of the present application, “at least one of A and B” may mean “at least one of A or B” or “at least one of combinations of one or more of A and B.” Furthermore, in the embodiments of the present application, “at least one of A and B” may mean “at least one of A or B” or “at least one of combinations of one or more of A and B.”

[0055] When a component is referred to as being "connected" or "connected" to another component, it should be understood that it may be directly connected or connected to that other component, but that there may be other components intervening. Conversely, when a component is referred to as being "directly connected" or "connected" to another component, it should be understood that there are no other components intervening.

[0056] The terminology used in this application is only used to describe specific embodiments and is not intended to limit the present invention. The singular expression includes the plural expression unless the context clearly indicates otherwise. In this application, it should be understood that the terms "comprise" or "have" indicate the presence of a feature, number, step, operation, component, part, or combination thereof described in the specification, but do not exclude in advance the possibility of the presence or addition of one or more other features, numbers, steps, operations, components, parts, or combinations thereof.

[0057] Unless otherwise defined, all terms used herein, including technical or scientific terms, have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. Terms defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant technology, and will not be interpreted in an idealized or overly formal sense unless explicitly defined herein.

[0058] Some terms used in this specification are defined as follows:

[0059] Hydrogen fueled vehicles generally include both hydrogen electric vehicles or hydrogen fuel cell electric vehicles (FCEVs) that use fuel cells, as well as ICE (Internal Combustion Engine)-based vehicles that use hydrogen as fuel.

[0060] Additionally, hydrogen fueled mobility refers to mobility that uses hydrogen as a fuel, and mobility can include automobiles in the conventional sense, automobiles, and hybrid vehicles that can utilize both human power and fuel.

[0061] In the following embodiments, a hydrogen fueling protocol and / or a communication protocol for hydrogen fueling may be applied in hydrogen fuel mobility.

[0062] The hydrogen fluid fuel may include gaseous hydrogen fuel or liquid hydrogen fuel.

[0063] Compressed Hydrogen Storage System (CHSS) refers to a device that compresses and stores hydrogen as part of the vehicle / mobility side.

[0064] A pressure relief device (PRD) is placed in the CHSS and is capable of isolating the stored hydrogen from the rest of the fuel system and environment, or, conversely, releasing the hydrogen to the outside.

[0065] Hydrogen charging basically refers to the process of supplying high-pressure hydrogen from a dispenser at a hydrogen charging station and compressing and storing it in a vehicle's tank. In the context of supplying hydrogen fuel to a hydrogen electric vehicle, "hydrogen charging" can be used interchangeably with the term "fueling." That is, in this specification, "fueling" can refer to fuel supply, hydrogen charging, or charging, and "charging" can refer to the charging of hydrogen fuel. For example, a fueling protocol can be referred to as a "charging protocol," a fueling session can be referred to as a "charging session," and a fueling method can be referred to as a "hydrogen charging method" or "fueling method."

[0066] Pressure Ramp Rate (PRR) is expressed in MPa / min and refers to the pressure increase rate of CHSS.

[0067] Average Pressure Ramp Rate (APRR) refers to the average pressure ramp rate from the beginning to the end of hydrogen fueling.

[0068] Pre-cooling refers to the process of cooling hydrogen in advance at a hydrogen charging station before charging.

[0069] A dispenser is a component that delivers pre-cooled hydrogen to the CHSS. The dispenser is placed at a hydrogen charging station and can perform hydrogen charging operations between the station's hydrogen storage tank and the vehicle's CHSS.

[0070] The nozzle is a device that is connected to the dispenser and is coupled to the receptacle of the hydrogen electric vehicle and allows the delivery of hydrogen fuel.

[0071] A fueling session may be used to mean communication sessions that occur across use cases for hydrogen fueling.

[0072] Interoperability can refer to the state in which components of a system can work together to achieve the intended function of the entire system. Information interoperability can refer to the ability of two or more networks, systems, devices, applications, or components to share information securely and effectively and easily with little or no user inconvenience.

[0073] Correlation / Association may include the process of establishing a relationship between two peer communication entities.

[0074] Command and control communication may refer to communication between an electric vehicle hydrogen fueling device and a hydrogen electric vehicle that exchanges information necessary for starting, controlling, and terminating the hydrogen fueling process.

[0075] Meanwhile, in the detailed description below, for the convenience of explanation, embodiments related to hydrogen electric vehicles or fuel cell electric vehicles (FCEVs) may be illustrated, but it will be apparent to those skilled in the art that the spirit of the present invention can be applied to various types of hydrogen fueled mobility. Hydrogen fueled mobility refers to mobility that uses hydrogen as an energy source or generates electric energy using hydrogen as fuel and uses this to drive an electric motor. In addition to hydrogen electric vehicles, hydrogen fueled mobility may include aerial mobility, as well as industrial trucks, trains, ships, and aircraft, which produce electric energy using hydrogen as fuel and use this to drive devices.

[0076] In addition, the two-way communication process for hydrogen fuel supply of the present invention can be partially applied not only to hydrogen fuel mobility but also to buildings or facilities that use hydrogen as an energy source.

[0077] Additionally, in the following description, hydrogen fuel may include at least one of gaseous hydrogen and liquid hydrogen, and may basically mean compressed hydrogen, but is not limited thereto.

[0078] In addition, vehicles that utilize the hydrogen fuel supply communication two-way process are described with a focus on hydrogen electric vehicles (FCEVs) for convenience of explanation, but are not limited to this configuration and may also include hybrid EV (electric vehicle) vehicles that use hydrogen as fuel, and ICE (internal combustion engine) vehicles.

[0079] In the following specification, some or all of the communication method, communication protocol negotiation method, hydrogen charging (fueling) protocol negotiation method, and hydrogen charging (fueling) parameter negotiation method performed in hydrogen fuel mobility may be performed by an electronic control unit (ECU), a communication device, or a communication control device in hydrogen fuel mobility.

[0080] In the following specification, some or all of the processes of the communication method, the communication protocol negotiation method, the hydrogen charging (fueling) protocol negotiation method, the hydrogen charging (fueling) parameter negotiation method, the hydrogen charging (fueling) method, and the hydrogen charging (fueling) control method performed in the dispenser may be performed by a controller, an electronic control unit, a communication unit, or a communication control unit of the dispenser. In addition, some processes of the above methods may be performed by a controller, an electronic control unit, a communication unit, or a communication control unit of a charging station associated with the dispenser.

[0081] Meanwhile, even if a technology was known before the filing date of the present application, it may be included as a part of the composition of the present application invention if necessary, and this will be described in this specification to the extent that it does not obscure the spirit of the present invention. However, in describing the composition of the present application, a detailed description of matters that were known before the filing date of the present application and that can be clearly understood by those skilled in the art may obscure the spirit of the present invention, and therefore, an excessively detailed description of the known technology will be omitted. For example, a technology that uses a thermodynamic model for hydrogen charging control, a technology that applies a model prediction control (MPC) technique for generalized dynamic control, a technology that configures and controls an artificial neural network for training and inference of the artificial neural network, etc., can utilize technology known before the filing date of the present application, and at least some of these known technologies can be applied as element technologies necessary for practicing the present invention.

[0082] However, the purpose of the present invention is not to claim rights to these known technologies, and the contents of the known technologies may be included as part of the present invention within a scope that does not deviate from the purpose of the present invention.

[0083] Hereinafter, a preferred embodiment according to the present invention will be described in detail with reference to the attached drawings.

[0084] FIG. 1 is a conceptual diagram illustrating a hydrogen fuel supply and control process for hydrogen fueled mobility to which one embodiment of the present invention is applied.

[0085] Referring to Fig. 1, pre-cooled hydrogen gas from a hydrogen charging station (Station, 200) is supplied to a hydrogen vehicle (Hydrogen fueled mobility / vehicle, 300) via a dispenser (100). At this time, the hydrogen fuel supply process can be described by parameters including the average pressure increase rate (APRR).

[0086] Hydrogen storage systems typically installed in vehicles can be broadly categorized into high-pressure hydrogen tanks, pressure control devices, high-pressure piping, and external frames. High-pressure hydrogen tanks have been developed and commercialized with capacities ranging from tens to hundreds of liters. For vehicles, small and lightweight storage tanks are connected in parallel to achieve high capacity.

[0087] A high-pressure hydrogen tank is widely known as a compressed hydrogen storage system (CHSS) (310). In this specification, for the convenience of explanation, the expression “tank” means CHSS (310), and for the convenience of explanation, the expressions “tank” and CHSS (310) may be used interchangeably.

[0088] In a typical hydrogen storage system, hydrogen storage is controlled by utilizing a boss unit that allows hydrogen gas to enter and exit the storage tank (310). Since hydrogen injection and use cannot be performed simultaneously, a valve, a pressure reducing device, and various sensors for measurement are attached to one boss unit to control hydrogen storage.

[0089] The interface between the hydrogen charging station (200) and the hydrogen fuel mobility (300) is handled by the dispenser (100), where the vehicle storage tank information and the fuel supply information of the charging station (200) are integrated to control the target pressure and injection speed, etc. An example of the control logic currently used is the control logic that follows the SAE J2601 (2020-05) standard.

[0090] In the prior art, there are two methods for transmitting information from a hydrogen fuel mobility (300) to a dispenser (100): a communication method and a non-communication method. Even when communication is used, in the prior art, the temperature and pressure values ​​of the vehicle storage tank (310) are simply transmitted in one direction to the dispenser (100), and the dispenser (100) does not actively utilize the information, but only uses it as a safety standard, such as an emergency stop at a temperature and pressure limit.

[0091] All charging logic for safe and rapid charging is managed in the dispenser (100), and the vehicle storage tank (310) has only the minimum safety management device that automatically releases hydrogen in conditions such as overheating through a pressure relief device (PRD) (320) without an active safety management method.

[0092] In order to cope with the phenomenon of the temperature of hydrogen gas rising during the hydrogen fuel supply described later in Fig. 2, the charging station (200) includes a high-pressure hydrogen storage unit (220) and a pre-cooler (210). The pre-cooler (210) supplies hydrogen gas to the hydrogen electric vehicle (300) via the dispenser (100) in a state where the temperature of the hydrogen gas is lowered through pre-cooling.

[0093] In one embodiment of the present invention, a basic configuration similar to that of the prior art is used, but a hydrogen fuel supply controller (110) inside a dispenser (100) actively controls a hydrogen fueling process by utilizing state information such as temperature and pressure received from a hydrogen fuel mobility (300) and a charging station (200), and state of charge information such as a state of charge (SOC, State of Charge) of a CHSS (310).

[0094] According to one embodiment of the present invention, the charging speed is controlled in real time by utilizing real-time temperature data of the vehicle storage tank (310), and is designed to operate at the highest charging speed that satisfies conditions below the safety limit, thereby reducing the charging time within the available range.

[0095] The charging protocol of the prior art has a problem in that the boundary conditions for safety are excessively set, so that the temperature of most storage tanks (310) is measured to be around 40 to 50°C at the time of charging completion, and thus the charging is supplied after excessive pre-cooling.

[0096] According to one embodiment of the present invention, the cooling load of the charging station (200) can be optimized by actively controlling the amount of pre-cooling required and the amount provided, thereby increasing the operating efficiency of the hydrogen charging station (200).

[0097] Conventional protocols centered on lightweight hydrogen electric vehicles have the problem that all variables must be reset and reflected in the standard when charging new mobility vehicles.

[0098] According to one embodiment of the present invention, a control technique that updates the logic itself through a certain learning and training process when applying a new device to an ANN-based learnable charging logic can be widely applied to various mobility areas.

[0099] The only measure to prevent overheating of the storage tank of a conventional hydrogen electric vehicle is to release gas through a pressure relief device / PRD (Pressure Relief Device) (320) when overheating exceeds a certain temperature.

[0100] According to one embodiment of the present invention, by installing a cooling system (330) to be described later in the storage tank (310) itself, the charging speed can be increased, and at the same time, overheating of the storage tank (310) can be actively dealt with, thereby improving the safety of hydrogen fuel mobility (300).

[0101] According to one embodiment of the present invention, it is possible to safely charge / supply hydrogen fuel while improving the efficiency of the hydrogen charging / supply process and improving the speed and real-time nature of the hydrogen charging / supply process.

[0102] According to one embodiment of the present invention, a real-time hydrogen charging control technique based on model predictive control (MPC) can be provided.

[0103] According to one embodiment of the present invention, a control technique based on an artificial neural network (ANN) model can be provided with improved accuracy in predicting hydrogen charging results. By incorporating real-time measured values ​​into an ANN model that utilizes actual fuel supply data along with theoretical simulation results, the accuracy of the prediction results can be improved.

[0104] According to one embodiment of the present invention, the efficiency of hydrogen charging control can be improved by integrating and managing actual measured data and status information predicted from a model using an intelligent meta system (IMS).

[0105] As described above, in the conventional hydrogen fuel supply process, the dispenser (100) is in charge of controlling between the hydrogen fuel mobility (300) and the hydrogen charging station (200), and the dispenser (100) is equipped with a protocol for injecting hydrogen into the hydrogen fuel mobility (300) according to a set rule, thereby overseeing the control.

[0106] An example of a protocol mounted on the dispenser (100) is a protocol based on the international standard SAE-J2601 (2020-05), which is also applied to embodiments of the present invention within the scope consistent with the purpose of the present invention.

[0107] For the minimum requirements for safety, simulations are conducted through thermodynamic modeling for various situations, and the parameters derived from this are utilized in a table-based single injection method and an MC-formula-based partial real-time correction method.

[0108] Minimum safety requirements / requirements include upper limits for temperature and pressure conditions of CHSS (310) and guidelines for state of charge (SOC).

[0109] Simulations can be performed through thermodynamic modeling using boundary conditions including Best to Worst Case.

[0110] This configuration can also be applied to the configuration of an embodiment of the present invention within the scope consistent with the purpose of the present invention.

[0111] Even if the configuration of Fig. 1 is followed, the following problems are found in the prior art that does not actively control the state value in the dispenser (100). The following problems are also revealed in the prior art that relies on simulation using a simple thermodynamic model.

[0112] The injection rate is predetermined based on the assumption of the worst-case boundary conditions (excessive boundary conditions), which leads to unnecessary pre-cooling and a decrease in the overall filling rate. In conventional technology, the injection rate is simply determined by the average pressure ramp rate (APRR), which can hinder proactive response to changing conditions. Unnecessary pre-cooling can also lead to excessive energy and operating costs.

[0113] Since it relies on simulation-based results, there are limitations on the capacity and shape of the applicable storage tank (310), and in the case of a new system, separate resources are required for new development and application, which limits the scope of application.

[0114] Thermodynamic models take a lot of time to derive mathematical calculation results, so they are limited in their application when there are no pre-calculated variables, as they indirectly utilize variables derived through the model. In addition, there is a lack of flexibility in the detailed adjustments to the method itself.

[0115] The table-based method of the prior art has a very low efficiency because it does not utilize the temperature of the pre-cooled hydrogen provided at the charging station (200) or the temperature of the storage tank (310) measured at the hydrogen fuel mobility (300), and has a problem in that it is difficult to flexibly respond to changes in the surrounding environment.

[0116] The MC-Formula-based method of the prior art corrects the precooling temperature in real time, but has the problem of being difficult to expand due to the complex calculation and application method and limitations in the scope of application.

[0117] The protocol was developed with the primary goal of completing safe charging, so there is no alternative to actively control unexpected situations such as excessive pre-cooling or overheating of the storage tank (310), which causes problems such as increased operating costs due to overcooling and delayed charging due to overheating.

[0118] The present invention is characterized by being derived to solve the problems of the prior art, and is characterized by reducing dependence on simulation and attempting to actively control state variables by reflecting real-time measurement data.

[0119] FIG. 2 is a conceptual diagram illustrating an example of state changes that occur during a hydrogen fuel supply process for hydrogen fuel mobility (300) to which one embodiment of the present invention is applied.

[0120] Referring to Fig. 2, when hydrogen is injected into a hydrogen storage tank (310), the internal temperature rises due to compression heat, and as a result, the temperature of the hydrogen gas inside the storage tank (310) rises.

[0121] Temperature control in the hydrogen fuel supply process is achieved by supplying pre-cooled hydrogen gas and controlling the internal temperature of the storage tank (310) to be 85°C or lower at the time of final charging completion.

[0122] The storage tank (310) is configured to have low heat transfer efficiency of the carbon fiber covering the dome and body of the tank (310) in order to block heat exchange between the external atmosphere and the hydrogen gas stored inside during driving.

[0123] When the temperature of the hydrogen gas inside the storage tank (310) rises during the fuel supply process, the temperature rise revealed on the surface of the hydrogen storage tank (310) is insignificant compared to the temperature rise inside until the charging is completed due to the low heat transfer characteristics of the storage tank (310).

[0124] These characteristics prevent heat exchange with the outside air, which can alleviate the rapid temperature rise inside the hydrogen storage tank (310) that occurs during charging, from occurring, so separate temperature management measures are required.

[0125] However, the prior art does not include a separate cooling means other than supplying pre-cooled hydrogen gas from a charging station (200).

[0126] The hydrogen buffer time is managed by controlling the precooling and hydrogen injection speed at the hydrogen charging station (200) to keep the temperature below 85°C, which is the upper limit of the temperature management of the vehicle hydrogen storage tank, but there is no separate temperature management measure for the storage tank (310) of the hydrogen fuel mobility (300).

[0127] Due to this, especially in the summer when the outside temperature is high, it is difficult to control the temperature of the vehicle storage tank (310) at the charging station (200), which causes problems such as charging delays.

[0128] In the embodiment of the present invention, the characteristic curve of FIG. 2 is installed as a basic model, but unlike the conventional technology, real-time data according to variables (outside temperature, air pressure, weather conditions, etc.) appearing in the actual surrounding environment are taken into consideration to optimize the control of the operating load of the charging station (200) in the Phase I precooling stage and the charging speed (pressure increase rate, PRR) occurring in the fuel supply process of Phase II to Phase IV, and optimal control conditions suitable for the actual environment can be derived.

[0129] FIG. 3 is a conceptual diagram illustrating a platform or test platform to which a hydrogen fuel supply process according to one embodiment of the present invention is applied.

[0130] Referring to FIG. 3, an embodiment is shown in which an artificial neural network model (120) is placed in addition to a hydrogen fuel supply controller (110) within a dispenser (100).

[0131] When the artificial neural network model (120) performs a model prediction control (MPC) process to predict the next state value, hydrogen fuel supply control by the ANN-MPC method can be implemented. Assuming that a fuel supply protocol that allows ANN-MPC-based hydrogen fuel supply control is used in FIG. 3, the ANN-MPC-based hydrogen fuel supply protocol is typically implemented in the dispenser (100) between the charging station (200) and the mobility (300). In an alternative embodiment of the present invention, the ANN-MPC-based hydrogen fuel supply protocol can be implemented in the hydrogen fuel supply controller (110) or the artificial neural network model (120). Additionally, in an alternative embodiment of the present invention, the ANN-MPC based hydrogen fueling protocol, hydrogen fueling controller (110), or artificial neural network model (120) may be implemented as part of a controller that is capable of electronically communicating with the dispenser (100) and influencing the hydrogen fueling process within the dispenser (100), even if not disposed within the dispenser (100).

[0132] Referring again to FIG. 3, the artificial neural network model (120) can receive data from a charging station (200) and / or mobility (300) using real-time communication, and use the data to transmit information for controlling hydrogen fuel supply to the hydrogen fuel supply controller (110) for optimal hydrogen fuel supply.

[0133] At this time, real-time communication data is composed of static and dynamic data. Static data is composed of data such as the capacity, configuration, or type of equipment of a storage tank that does not change over time, and dynamic data may refer to data such as temperature or pressure that may change during the fuel supply process.

[0134] Static data may include, for example, the volume of the tank of the mobility (300) or the charging station (200), the pressure rating, the maximum pressure allowed, the type of the tank, the number of tanks, the size of the tank, the serial number of the tank, the manufacturer of the tank, the usage data of the tank, the allowable temperature range of the hydrogen fluid in the tank, etc.

[0135] Dynamic data may include, for example, fueling commands (e.g., start, stop, pause, abandon, increase flow, decrease flow, tank change commands, etc.), control parameters for fueling (e.g., average pressure ramp rate (APRR), pressure ramp rate (PR), etc.), real-time measurements of pressure and / or temperature within the tank, including fluctuations in temperature and / or pressure within the tank (which may indicate a leak and / or impending failure), state of charge (SOC) of the tank at the charging station or mobility side, ambient temperature outside the tank, and / or real-time measurements of the flow rate of hydrogen fluid flowing into the tank.

[0136] Referring again to FIG. 3, a hydrogen fuel supply control method based on an ANN-MPC-based fuel supply protocol can set basic conditions required for hydrogen fuel supply using static data before starting hydrogen fuel supply, and perform real-time control based on dynamic data during the hydrogen fuel supply process.

[0137] Referring to Fig. 3, an exception and problem situation handling module (400) can be added. The ANN-MPC model basically assumes real-time two-way communication, and can exhibit optimal performance under conditions where real-time two-way communication is possible.

[0138] However, various situations that deviate from real-time communication conditions may occur during the verification process before starting hydrogen fueling or during actual hydrogen fueling, as follows.

[0139] (1) No communication situation: When the mobility (300) or charging station (200) is not equipped with a communication function, the communication function is broken, or the communication function is broken during fuel supply and communication is impossible.

[0140] (2) Communication error: Communication with the mobility (300) or charging station (200) is possible, but parts, sensors, or equipment are not functioning properly, causing some of the data to be missing or to have errors.

[0141] (3) Non-certification of equipment, parts, or vehicles: When communication with mobility (300) or charging station (200) is possible and data is collected normally, but the reliability of data transmitted from mobility (300) or equipment is low, requiring verification or limited use.

[0142] ④ Uncertified communication protocol: Communication with mobility (300) or charging station (200) is possible, but the communication method is not specified in the standard or is not certified in the country, so safety, reliability, and stability are not secured.

[0143] Although the real-time communication-based control of ANN-MPC can have optimal performance in terms of functionality, in order to apply it to actual fields, a method that can effectively, step by step, and differentially respond to communication problems that may occur in various forms is required, and a means to solve such problems is proposed in the embodiment described below.

[0144] FIG. 4 is a conceptual diagram illustrating a model prediction control (MPC) process according to one embodiment of the present invention.

[0145] In the control method using MPC, the accuracy of control increases as the accuracy of the prediction model increases. The control method illustrated in Fig. 4 is ANN-MPC, which uses an artificial neural network model (120) as a prediction model and a feedback control loop including a hydrogen fuel supply controller (110) as a control method.

[0146] Referring to FIG. 4, the temperature, pressure, and outside temperature of the mobility (300) and the charging station (200) are used as inputs, and the temperature and pressure of the storage tank (310) of the mobility (300) are set as outputs.

[0147] In FIG. 4, an embodiment is shown in which the temperature and pressure of the hydrogen mobility (300) are predicted as output, but in an alternative embodiment of the present invention, an embodiment in which the temperature and pressure of the storage tank of the charging station (200) are predicted as output can be implemented.

[0148] When a new input is given, the output can be predicted by an artificial neural network model (120) that learns the correlation between input and output using simulation data by a theoretical model and data measured in an actual field.

[0149] When the SOC value (target value) specified in the controller (110) is given, the pressure ramp rate (PRR), which is the charging speed to achieve the target value, can be predicted. When PRR_pd is given as a control parameter, the artificial neural network model (120) can predict SOC_pd. SOC_pd is input to the controller (110) to replace the initial target SOC_sp. At this time, the SOC_m of the mobility (300) side where PRR_c is actually applied and hydrogen fuel supply is performed is transmitted to the artificial neural network model (120) so that the artificial neural network model (120) can be trained to improve the prediction accuracy of SOC_pd as an objective function or loss function.

[0150] Referring to FIGS. 3 and 4 together, real-time temperature, pressure, supply rate (which may mean PRR) on the side of the charging station (200), and abnormal signals may be transmitted to the artificial neural network model (120). In an alternative embodiment of the present invention, real-time temperature, pressure, supply rate (which may mean PRR) on the side of the charging station (200), and abnormal signals may also be transmitted to the hydrogen fuel supply controller (110).

[0151] Real-time temperature, pressure, and abnormal signals of the mobility (300) can be transmitted to the artificial neural network model (120). In an alternative embodiment of the present invention, real-time temperature, pressure, and abnormal signals of the mobility (300) can also be transmitted to the hydrogen fuel supply controller (110).

[0152] The hydrogen fuel supply controller (110) generates an actual control command, and hydrogen fuel supply of the dispenser (100) is performed based on the control command, and part of the control command, for example, a temperature down command, can also be transmitted to the mobility (300) side.

[0153] At this time, the command or information related to the control of hydrogen fuel supply may include a pressure ramp rate (PRR) for hydrogen fuel supply in the tank (310) of the mobility (300), a control command for the pressure ramp rate, and / or status information on the dispenser (100) side as a result of executing the control command. In addition, the information related to the control of hydrogen fuel supply may include information on variables that may affect changes in the state of hydrogen in the vehicle tank (310) of the mobility (300), such as a real-time pressure ramp rate (PRR) derived from a feedback control process or a mass flow rate of compressed hydrogen (kg / s) [m_dot], and / or information on the result of executing control by the variables. These hydrogen fuel supply control-related information may also affect the weights or parameters of the hidden layer of the artificial neural network model (120).

[0154] A hydrogen fuel supply control request based on a hydrogen fuel supply protocol may include a control command for a pressure ramp rate (PRR) for charging hydrogen in a vehicle tank (310).

[0155] Information related to hydrogen fuel supply control may include at least one of information on a hydrogen fuel supply control request and status information according to the execution result of the hydrogen fuel supply control request, and a control request for a change in the status of hydrogen may include a control request for at least one of the temperature and pressure of hydrogen in the vehicle tank (310).

[0156] Changes in the state of hydrogen in the vehicle tank (310) may include at least one of temperature, pressure, and state of charge (SOC).

[0157] As a theoretical simulation, a thermodynamic model such as H2FillS (Hydrogen Filling Simulation) can be used, for example. The thermodynamic model can include a model and / or software designed to track and report transient changes in at least one of hydrogen temperature, pressure, and mass flow and / or transient changes in the state of hydrogen within a vehicle tank when fueling a hydrogen fueled vehicle / mobility. Of course, the spirit of the present invention is not limited to embodiments of a specific thermodynamic model.

[0158] The hydrogen fueling protocol may include, for example, the hydrogen charging protocol defined in SAE J2601.

[0159] A thermodynamic model can generate output data based on modeling and simulation when input data, such as those of an artificial neural network, is input. The parameters of the thermodynamic model can be adjusted according to the hydrogen charging protocol, and different hydrogen charging protocols with the same input data can produce different output data.

[0160] In one embodiment of the present invention, on-site data collected by a test platform is provided as input and output data of an artificial neural network, instead of input / output data of a thermodynamic model, so that the artificial neural network can be trained. That is, some of the on-site data collected by the test platform can be provided as input data for the artificial neural network, and other parts can be provided as ground truth data corresponding to the output data of the artificial neural network.

[0161] The internal parameters of an artificial neural network can be trained without initialization, or they can be initialized to predetermined values ​​and then trained based on field data. For example, the input and output data (ground truth data) of an artificial neural network can be given and initially trained based on a thermodynamic model, thereby initializing the internal parameters of the artificial neural network.

[0162] Learning in artificial neural networks does not necessarily have to be deep learning; it can also be shallow learning.

[0163] A test platform according to one embodiment of the present invention may rely on dynamic field data to optimize the hydrogen fueling process.

[0164] One embodiment of the present invention can predict the next state using an artificial neural network-based model predictive control (MPC) technique. In this case, the artificial neural network model (120) can be trained using theoretical results, on-site data, or both.

[0165] The artificial neural network model (120) may be a model trained to receive information related to hydrogen fuel supply control and information on the state of hydrogen in a vehicle tank (310) related to the result of executing a hydrogen fuel supply control request, and to predict changes in the state of hydrogen in the vehicle tank (310).

[0166] The artificial neural network model (120) may be a model trained to receive information related to hydrogen fuel supply control, the current state of hydrogen in the vehicle tank (310), the state of hydrogen supplied from the dispenser (100) to the mobility (300), and the ambient temperature, and to predict changes in the state of hydrogen in the vehicle tank (310) in the future.

[0167] The artificial neural network model (120) may be a model trained with the function of predicting changes in the state of hydrogen in the vehicle tank (310) according to each target state of the state of hydrogen in the vehicle tank (310) and each hydrogen fuel supply protocol, which are related to information on hydrogen fuel supply control.

[0168] The artificial neural network model (120) receives information related to hydrogen fuel supply control and information on the state of hydrogen in the vehicle tank (310), and trains a function to predict changes in the state of hydrogen in the vehicle tank (310) by using field data on changes in the state of hydrogen in the vehicle tank as ground truth data, thereby updating parameters in the model (120).

[0169] The artificial neural network model (120) may be a model trained to predict changes in the state of hydrogen in a vehicle tank (310) using a model prediction control technique.

[0170] In cases where the pre-cooling function of the charging station (200) or the cooling system (310) of the mobility (300) is not available, the control of the hydrogen fuel supply can be performed by reflecting these factors.

[0171] Big data is collected for each type and individual ID of the dispenser (100), type and individual ID of the charging station (200), type and individual ID of the mobility (300), control target state (temperature, pressure, SOC), initial state (temperature, pressure), and type of hydrogen fuel supply protocol, and the test platform is trained using dynamic changes in field data corresponding to each case, thereby deriving standardized items that can optimize and accurately describe the hydrogen fuel supply process.

[0172] One embodiment of the present invention can improve the reliability of a hydrogen fuel supply process by collecting and processing on-site hydrogen fuel supply data.

[0173] Field data on changes in the state of hydrogen within the vehicle tank (310) can be first acquired from the mobility (300) side. That is, field data on changes in the state of hydrogen within the vehicle tank (310) can be acquired from the mobility (300) side regardless of whether a control request is transmitted to the hydrogen vehicle. Alternatively, in another embodiment of the present invention, the control request may include a field data request, and the mobility (300) side may acquire field data in response to the control request / field data request.

[0174] Acquisition of field data can be performed not only on the mobility (300) side but also on the charging station side, and similarly, field data on the charging station side can be collected regardless of the control request, and the control request can include a field data request, and field data on the status of hydrogen stored in the charging station and / or hydrogen supplied from the charging station to the dispenser can be acquired in response to the control request / field data request on the charging station side.

[0175] On-site data refers to data obtained from a charging station (200) and / or mobility (300) during the actual hydrogen fuel supply / supply process. In this case, on-site data may include data obtained in cases where some or all of the environment is a test environment, in addition to cases where the environment is an actual charging station or an actual vehicle.

[0176] Field data may include data obtained from a test environment or execution device that may be equipped with a test environment or response (feedback) capable device corresponding to the hydrogen vehicle side, a test environment or response (feedback) capable device corresponding to the hydrogen storage cylinder and dispenser side of the charging station, and / or a module A corresponding to the dispensing control system side.

[0177] At this time, the field data may include data obtained from a fuel supply field or test environment in which all devices are a test environment composed of the above three test environments or devices, or which includes any one of them.

[0178] For example, field data may include data obtained from a test environment or fuel supply site comprised of simulation models linked to module B (150) and module C (160) illustrated in FIG. 5.

[0179] At this time, a device capable of responding (feedback) may mean a device that includes a database built based on actual field data and can respond according to a case requested by module A.

[0180] Field data may include static data and dynamic data, and may be classified into static data (type of vehicle tank (310), volume of vehicle tank (310), number of vehicle tank (310) modules / banks, etc.) and dynamic data (vehicle tank (310) hydrogen temperature, pressure, etc.).

[0181] A charging station (200) may be equipped with multiple hydrogen storage cylinders and operated as a bank system. Real-time on-site information requested and received from the charging station (200) by the dispenser (100) may include temperature and pressure information for each bank.

[0182] Multiple banks can be switched and connected to the dispenser (100) according to a request from the dispenser (100) and / or a selection from the charging station (200). At this time, temperature and pressure information for each bank included in the real-time field information that the dispenser (100) requests and receives feedback from the charging station (200) can influence the selection and / or switching of the banks.

[0183] At this time, the status of at least one bank in the bank system can be adjusted based on the temperature and pressure information for each bank included in the real-time field information that the dispenser (100) requests and receives feedback from the charging station (200).

[0184] For example, as a preparatory step, at least one or more banks within the charging station (200) may be adjusted to have a chargeable temperature and pressure based on current status information about the banks. Such adjustment may be performed at the request of the dispenser (100) or by the control logic of the charging station (200).

[0185] FIG. 5 is a conceptual diagram illustrating a platform or test platform for simulation used in a hydrogen fuel supply process according to one embodiment of the present invention.

[0186] Referring to FIG. 5, a mobility part (300a) for simulating actual mobility (300), a charging station part (200a) for simulating an actual charging station (200), and a dispenser part (100a) for simulating an actual dispenser (100) are illustrated.

[0187] In Fig. 5, the mobility part (300a), the charging station part (200a), and the dispenser part (100a) may be implemented by merging, combining, or competing between a simulation model and a data-based model based on real-time on-site dynamic data.

[0188] In order to analyze the process state change between the dispenser (100) and the charging station (200), a learning model based on field data between the dispenser (100) and the mobility (300) can serve as a reference for the interaction process between the dispenser part (100a) and the mobility part (300a).

[0189] Conversely, in order to analyze the process state change between the dispenser (100) and the mobility (300), a learning model based on field data between the dispenser (100) and the charging station (200) can serve as a reference for the interaction process between the dispenser part (100a) and the charging station part (200a).

[0190] In an alternative embodiment of the present invention, the integrated management system controlling the simulation of FIG. 5 may be implemented in the form of a cloud and / or remote server.

[0191] In addition, although an embodiment centered on an artificial neural network model (120) is illustrated in the present application specification, other embodiments of the present invention need not be limited by the artificial neural network model (120) or the model predictive control technique. In another embodiment of the present invention, after hydrogen fuel supply control-related information and / or feedback control based on a hydrogen fuel supply protocol is transmitted, real-time field data is fed back as a response, and the hydrogen fuel supply control-related information and / or feedback control may be updated based on the real-time field data, or the next hydrogen fuel supply control-related information and / or feedback control may be generated. According to another embodiment of the present invention, a hydrogen fuel supply protocol that does not rely on model predictive control or an artificial neural network model (120), and a test method and a test platform for testing a hydrogen fuel supply system may also be proposed.

[0192] When the charging station part (200a) functions as a simulation model, the mobility part (300a) can function as a modulation and optimization means for the temperature of the CHSS (310).

[0193] When the mobility part (300a) functions as a simulation model, the charging station part (200a) can function as a means for stabilizing and controlling the precooling temperature.

[0194] The integrated management system controlling the simulation of Fig. 5 can obtain the difference between the simulation results for changes in the state of hydrogen in the vehicle tank corresponding to hydrogen charging control-related information and field data for changes in the state of hydrogen in the vehicle tank.

[0195] An integrated management system controlling the simulation of FIG. 5 can update a model (120) for a hydrogen fueling process for a hydrogen vehicle corresponding to information related to hydrogen charging control based on differences between simulation results and field data.

[0196] An integrated management system controlling the simulation of FIG. 5 can obtain simulation results for changes in the state of hydrogen in a vehicle tank in response to a control request by using a thermodynamic model that tracks transient changes in at least one of temperature, pressure, and mass flow of hydrogen in a vehicle tank.

[0197] The integrated management system controlling the simulation of Fig. 5 can obtain simulation results for changes in the state of hydrogen in a vehicle tank in response to a hydrogen charging control request by inputting a future predicted hydrogen charging control request into a model for a hydrogen fueling process using a model prediction control (MPC) technique.

[0198] The integrated management system controlling the simulation of FIG. 5 can, after updating the model (120), replace the hydrogen fuel mobility (300) side with the model (120) to obtain at least one of simulation data and field data of a change in the state of hydrogen supplied to the dispenser (100) from the station (200) corresponding to the second control request between the dispenser (100) supplying hydrogen to the hydrogen fuel mobility (300) and the station (200) supplying hydrogen to the dispenser (100).

[0199] The integrated management system controlling the simulation of FIG. 5 can obtain at least one of simulation data and field data of changes in the state of hydrogen in a vehicle tank of a hydrogen fuel mobility (300) that receives hydrogen from a dispenser (100) and responds to a third control request between the hydrogen fuel mobility (300) and the dispenser (100) by replacing the station (200) side with the model (120) after updating the model (120).

[0200] Conventional hydrogen fueling processes or hydrogen charging control techniques have difficulties in controlling the final SOC, final nozzle, and CHSS (310) temperature / pressure as desired. Furthermore, theoretical simulation-based hydrogen charging techniques often fail to match actual field data due to pressure variability, unstable flow rates, and high environmental variability.

[0201] The discrepancy between simulation results and actual field data is easily affected by the characteristics of the device and the diversity of the environment, which are difficult to sufficiently consider in theoretical simulations. Even when using the same hydrogen charging protocol, the final field data may differ depending on the initial value or final target value. Conversely, even when assuming the same final target value or initial value, the final field data may differ depending on different hydrogen charging protocols.

[0202] To solve these problems of the prior art, one embodiment of the present invention may adopt utilization of real-time field data, two-way communication between each device, predictive control techniques, integrated control of the entire system including stations and vehicles, and utilization and standardization of hydrogen fuel supply data based on user needs.

[0203] Additionally, one embodiment of the present invention may enable enhancement of an existing hydrogen fueling protocol, standardization of on-site hydrogen fueling data format, and compilation and diagnosis of dynamic field data.

[0204] In relation to enhancing existing hydrogen charging protocols, one embodiment of the present invention may include the following:

[0205] Existing hydrogen charging protocols can be selected for testing.

[0206] One embodiment of the present invention executes artificial neural network-based model predictive control (ANN-MPC) and executes an existing hydrogen charging protocol under the same charging conditions, thereby comparing the results of charging control using ANN-MPC with the results of charging control using the existing hydrogen charging protocol, thereby strengthening or improving the existing hydrogen charging protocol.

[0207] The supervisory system (130) can embed a protocol (131) to be tested and can control charging based on the embedded protocol and compare the control value with the 'predicted output' input from the ANN-MPC.

[0208] The supervisory system (130) or controller can transmit hydrogen charging control related information to the hydrogen fuel mobility (300) via the communication interface (140, 160) by executing an existing hydrogen charging protocol, and can obtain field data on changes in the state of hydrogen in the vehicle tank via the communication interface (140, 160).

[0209] The supervisory system (130) or controller can obtain a hydrogen charging control sequence by a model (120) for a hydrogen fueling process (if using the ANN-MPC technique, future time series control sequence inputs can be predicted), and based on the hydrogen charging control sequence by the model (120) for a hydrogen fueling process, an existing hydrogen charging protocol can be enhanced.

[0210] The supervisory system (130) or controller can obtain result data by executing a hydrogen charging control sequence by a model for a hydrogen fueling process, and based on a comparison result between the result data by executing the hydrogen charging control sequence and field data by an existing hydrogen charging protocol, can strengthen a part of an existing hydrogen charging protocol using the hydrogen charging control sequence.

[0211] At this time, the charging control result using the artificial neural network model (120) can be obtained by operating the model (120) or can be obtained from a pre-built database. The basic specifications used in the charging protocol (131) to be tested are not changed, and the details, i.e., the charging table or logic, can be strengthened / improved by referring to the charging control values ​​of the artificial neural network model (120). At this time, the charging control result using the artificial neural network model (120) and the field data obtained as the result of executing the charging protocol (131) to be tested are compared, and if the performance of the charging control result using the artificial neural network model (120) is superior, the details of the charging protocol (131) to be tested can be partially improved. A comparison between the charging control result and the execution result of the charging protocol (131) to be tested can also be performed for all or part of the hydrogen fuel supply process.

[0212] In one embodiment of the present invention, a Lumped Thermodynamic Model for an artificial neural network for dispenser-vehicle interaction can be utilized by considering the following features.

[0213] - 0-Dimensional Unsteady State Mass & Energy Balance

[0214] - 1-Dimensional Heat Transfer for Vehicle Tank Wall

[0215] - CoolProp for Evaluation of Hydrogen Properties

[0216] One embodiment of the present invention can perform comparative analysis between theoretical simulation results and real on-site data.

[0217] One embodiment of the present invention can perform predictive analysis under specific conditions in addition to the conditions assumed in the hydrogen charging protocol.

[0218] FIG. 6 is a conceptual diagram illustrating a concept of modeling, predicting, or controlling a hydrogen fuel supply process using an artificial neural network (ANN) in a hydrogen fuel supply process according to one embodiment of the present invention.

[0219] Referring to Figure 6, measurement values ​​of the current state are input into the input layer.

[0220] At this time, the ambient temperature Tamb (ambient temperature), pre-cooling temperature Tpre (pre-cooled gas temperature), and pre-cooling pressure Tpre (pre-cooled gas pressure) can be measured at the nozzle of the dispenser (100) or the charging station (200).

[0221] Hydrogen gas temperature T CHSS , hydrogen gas pressure P CHSS is a value measured on the CHSS (310) side of hydrogen fuel mobility (300), and the actual measured value can be input to the input layer.

[0222] During the training process of an artificial neural network, the actual current measurement value is passed to the input layer, and the next measurement value is passed to the output layer, where it can be utilized as ground truth data 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 measurement values. The correlation between the data input to the input layer and the data provided to the output layer is learned, and through this, predictions using real dynamic fueling data along with theoretical results are possible.

[0223] In the inference or output process using an artificial neural network, the actually measured on-site measurement value is passed to the input layer, and a prediction value for the next measurement value can be obtained as an output by the operation of the artificial neural network.

[0224] The learning process of the artificial neural network used in the embodiment of the present invention may be either shallow learning or deep learning, and the artificial neural network may be a type of neural network that suits the purpose of the present invention among known neural networks.

[0225] The values ​​input through the input layer are passed to the output layer after going through the weight-based operation of the hidden layer.

[0226] The state values ​​(predicted values ​​for the next state) output by the output layer can be used to derive state-of-charge variables, for example, state-of-charge (SOC), using at least part of a thermodynamic model.

[0227] In the embodiment of the present invention, hybrid control combining a theoretical simulation model and an artificial neural network is also possible, so that a desired result can be achieved even through learning using a small amount of data, and performance suitable for the purpose of the present invention can be derived even through a lightweight artificial neural network.

[0228] The real-time pressure increase rate (PRR) or mass flow rate of compressed hydrogen (kg / s) [m_dot] derived from the feedback control process can affect the weights or parameters of the hidden layer of the artificial neural network.

[0229] The artificial neural network-based hydrogen charging technique of the present invention can improve the accuracy of model-based charging result predictions. By utilizing actual fuel supply data alongside theoretical simulation results, real-time measurements can be reflected, further improving the accuracy of prediction results.

[0230] While the control protocol of the prior art calculates and predicts results through simulations tailored to individual situations, the embodiment of the present invention differs in that it utilizes a process of improving accuracy through repeated training for various situations.

[0231] Due to these differences, in the embodiment of the present invention, as various theoretical values ​​and empirical results are added, the accuracy is gradually improved through updates, and even if a new fuel supply process using a new storage tank (310) configuration or a change in flow rate is introduced, the function can be updated in the model by adding actual data and training, so that it can be widely applied to various mobility fields.

[0232] As a hydrogen charging control technique for a hydrogen charging test for hydrogen fuel mobility (300) according to one embodiment of the present invention, model prediction control (MPC) may be used.

[0233] FIG. 7 is a conceptual diagram illustrating a concept of controlling a hydrogen fuel supply process to reach a target output using model predictive control in a hydrogen fuel supply process according to one embodiment of the present invention.

[0234] Referring to Figure 7, Model Predictive Control (MPC) is a control method that uses a model that simulates a process to predict outputs based on future inputs, then optimizes these outputs to derive control inputs. To ensure compliance with various boundary conditions during process control, the process response must remain within a defined range. Compared to other control methods, MPC can effectively ensure that the process response remains within a defined range.

[0235] MPC plans inputs for a prediction horizon to achieve a process response that approaches the target output. It then obtains the corresponding response through a prediction model, using only the control input as the control input. Once the plant receives output, it repeats the same process based on that output.

[0236] In an embodiment of the present invention, the accuracy of the hydrogen charging model is secured to a considerable level, and the future charging result is predicted from the hydrogen charging model and the current measurement value, and the temperature T of the hydrogen gas of CHSS (310) is determined based on the predicted value and the charging value. CHSS or pressure P of hydrogen gas CHSS The pressure ramp rate / rise rate (PRR) can be controlled in real time to reach the optimal filling target without violating constraints such as specific variables.

[0237] Referring to FIG. 7, in an embodiment of the present invention, an MPC-based control is used to calculate a future output value based on a current measurement value and a model's predicted value, and an operation parameter / variable can be adjusted so that the predicted future response moves to a setpoint (or target) in an optimal manner.

[0238] For example, at the current time i, n model predictions can be derived. These n model-based predictions form a prediction horizon.

[0239] Each model-based prediction, or prediction horizon, corresponds to a control horizon. That is, the n control commands / control actions required to achieve n model predictions can form a control horizon.

[0240] In practice, the first of the n model prediction and control actions derived at the current time i, the i+1 control action, can be transmitted to the system. As time passes, a new set of n model prediction and control actions are derived at the current time i+1, forming a new Prediction Horizon and Control Horizon, respectively.

[0241] The technique of controlling the system by expanding / moving the Horizon in this way is called MPC, and in the embodiment of the present invention, MPC-based control can be executed using measured values ​​and predicted values ​​for state information (state values) including the temperature and pressure of hydrogen gas of CHSS (310).

[0242] FIG. 8 is a flowchart illustrating the training and inference process of an artificial neural network when an artificial neural network is used for model prediction control in a hydrogen fuel supply process according to one embodiment of the present invention.

[0243] Referring to FIG. 8, a training process of an artificial neural network considering an artificial neural network-model predictive control (ANN-MPC) technique according to one embodiment of the present invention is illustrated.

[0244] In Fig. 8, we assume an artificial neural network that has learned the function of obtaining an MPC-based Prediction Horizon and a Control Horizon, and in particular, an artificial neural network that has learned the function of obtaining n future predictions and corresponding control commands so that the process of a future response reaching a set point is optimized by MPC.

[0245] Referring to FIGS. 6 to 8 together, in an embodiment of the present invention, a control system can be configured based on an ANN model (120), and a test platform system for real-time control based on model predictive control can be configured by ensuring the accuracy of the ANN model (120).

[0246] The test platform system for real-time control is a method of controlling the charging speed / pressure increase rate / pressure increase rate by predicting future charging results and comparing them with actual measured values. By separately setting restrictions, control time intervals, and sensitivity within the system logic, control can be achieved within optimal values.

[0247] First, optimal control is performed based on real-time data of the charging station (200) and hydrogen fuel mobility (300), but when a specific event situation occurs in the operation of the system, the function of directly controlling the precooling temperature of the precooler (210) and the cooling system of the hydrogen fuel mobility (300) can be provided, thereby increasing the overall efficiency of the hydrogen fuel supply process.

[0248] Referring to Figure 8, a control process is initiated by receiving a specific SOCsp from a customer (t=0, S710). For example, the current SOC may be 50% and the SOCsp may be 85%.

[0249] SOC(t) is T CHSS (t), and P CHSS It is given as a function of (t), and this process can be performed based on a general dynamic model.

[0250] If the current SOC(t) is greater than or equal to SOCsp (S720), hydrogen charging can be stopped. If the current SOC(t) is less than SOCsp (S720), i=t is set, and Moving Horizon Prediction using an artificial neural network is performed (S730).

[0251] Step S730 can be performed by generating an MPC prediction using an artificial neural network (120), etc. In step S740, it can be determined whether the obtained n predictions are optimized predictions that meet the intended purpose.

[0252] If the obtained n predictions are optimized predictions, a control command PRR(t) is obtained based on the n predictions and control commands, and PRR(t) can be applied to the dispenser (100)-vehicle tank (310) (S750).

[0253] After that, increase the time t and obtain a new measurement value T CHSS (t), and P CHSS (t) is obtained and passed as input to step S720.

[0254] If the n predictions obtained in step S730 are not optimized predictions, step S730 may be performed again to obtain n new predictions and control commands.

[0255] In step S730 of FIG. 8, state prediction values ​​(T, P) that satisfy temperature and pressure limits for all arbitrary i,k can be generated.

[0256] Based on the current time i(=t), n state prediction values ​​and corresponding control commands can be derived.

[0257] Step S740 of FIG. 8 can be understood as a process of searching for a set of n predictions that minimizes a cost function indicating whether the final control objective, SOCsp, has been reached.

[0258] As an output of hydrogen fuel mobility (300), state measurement values ​​including temperature and pressure of CHSS (310) can be provided as feedback input to an artificial neural network model (120).

[0259] The state measurement values ​​including the temperature and pressure of the pre-cooled hydrogen gas as an output of the hydrogen charging station (200) can be provided as feedback input to the artificial neural network model (120).

[0260] The artificial neural network model (120) transmits the predicted output to the hydrogen fuel supply controller (110), and the supervisory system (130) can input future inputs obtained through simulation or model-based prediction to the artificial neural network model (120) via module A (140).

[0261] The ANN-MPC-based control process is a control technique that utilizes simulation and actual measurement data together, and is a control technique that performs simulation at least partially using an artificial neural network model (120) and uses the prediction results in the control process.

[0262] An embodiment of the present invention aims to configure a real-time data-based integrated control protocol for hydrogen charging, and the system is implemented by utilizing various element technologies.

[0263] The protocol installed in the dispenser (100) can control the charging speed / pressure increase rate / pressure increase rate (PRR or [m_dot]) as output by using the data of the pre-cooled hydrogen gas provided from the charging station (200) and the data of the storage tank (310) provided from the hydrogen fuel mobility (300) as real-time input values ​​through the installed model.

[0264] When an event such as an external environmental change occurs, the pre-cooling temperature of the charging station (200) and the cooling system of the hydrogen fuel mobility (300) can be directly controlled to control the charging speed / pressure increase rate / pressure increase rate (PRR or [m_dot]) and process efficiency in general.

[0265] To complement the control protocol, the pre-cooling system / pre-cooler (210) of the hydrogen charging station (200) may be independently installed with its own cooling stabilization system.

[0266] In terms of temperature stabilization, the cooling stabilization system of the pre-cooler (210) can be independently controlled, and the target value of the control can be changed integrally in the protocol of the dispenser (100).

[0267] To improve the economic efficiency of the charging station (200) and to complement the functions of the integrated control protocol, the pre-cooler (210) may be provided with additional functions related to temperature stabilization.

[0268] The precooling temperature varies depending on the initial temperature and flow rate of hydrogen gas supplied to the precooler (210), and to compensate for this, a novel precooler structure for stabilizing the temperature is proposed as an embodiment of the present invention.

[0269] A pre-cooler (210) according to one embodiment of the present invention may include control logic for its own temperature control and linkage with a protocol.

[0270] A forced cooling system may be installed in the storage tank (310) of the hydrogen fuel mobility (300), and the charging speed may be improved by cooling some of the compression heat generated during hydrogen charging, and the operation / control of the forced cooling system of the storage tank (310) may also be involved in the protocol.

[0271] In one embodiment of the present invention, a temperature management function may be provided to the storage tank (310) of the hydrogen fuel mobility (300) to improve the hydrogen charging speed and complement the functions of the integrated control protocol.

[0272] In one embodiment of the present invention, the vehicle storage tank (310) may configure a system for self-cooling to increase the overall charging speed and improve the safety of hydrogen fuel mobility (300), and may include control logic for self-driving of the system and linkage with a protocol.

[0273] In one embodiment of the present invention, through such integrated control, not only can the current charging efficiency be improved, but preparation for the next charging can also be smoothly supported.

[0274] In the case of T40 where the precooling temperature of the pre-cooler (210) is set to -40°C, if the precooling temperature has achieved the target value but the outside 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 information on the current status can be transmitted to the hydrogen fuel mobility (300) / storage tank (310) side so that the self-cooling system of the storage tank (310) can be driven.

[0275] Conversely, if the precooling temperature of the pre-cooler (210) is set to -40°C but is judged to be excessive cooling when considering the external environment and actual data, the target value of the precooling temperature can be adjusted (for example, -35°C).

[0276] In cases where additional temperature control of the pre-cooling target temperature and the storage tank (310) side is required, control information or control commands can be transmitted from the dispenser (100) to both the hydrogen fuel mobility (300) and the charging station (200).

[0277] According to an embodiment of the present invention, the self-cooling system of the hydrogen fuel mobility (300) and the charging station (200) may be independently controlled, or may be controlled by transmitting a signal from the dispenser (100).

[0278] An integrated control method for hydrogen charging according to one embodiment of the present invention may further include a step of evaluating whether a measurement value of a current state satisfies a constraint condition.

[0279] The constraint may be that the temperature and pressure of the compressed hydrogen storage system on the hydrogen vehicle side do not exceed the limit temperature and limit pressure, respectively.

[0280] According to one embodiment of the present invention, it is possible to safely charge / supply hydrogen fuel while improving the efficiency of the hydrogen charging / supply process and improving the speed and real-time nature of the hydrogen charging / supply process.

[0281] According to one embodiment of the present invention, a test method and test platform capable of precisely modeling a hydrogen charging / supply process based on real-time on-site dynamic data can be implemented.

[0282] According to one embodiment of the present invention, a test method and a test platform can be implemented that provide a model capable of precisely controlling a hydrogen charging / supplying process by considering the difference between modeling and simulation results by a theoretical model and real-time field data, or by considering both modeling and simulation results and real-time field data.

[0283] According to one embodiment of the present invention, a test technique for real-time hydrogen charging control based on model predictive control (MPC) can be implemented.

[0284] According to one embodiment of the present invention, a test technique for control with improved accuracy in predicting hydrogen charging results based on an artificial neural network (ANN) model can be implemented.

[0285] By inputting a charge amount (e.g., SOC) directly set by the user, hydrogen can be charged / supplied until the target amount is reached through control logic based on real-time communication and calculation using a sequence such as that shown in FIGS. 6 to 8.

[0286] Various variables such as SOC, target pressure, time, and temperature can be used to set the charge amount, and the charge speed can be controlled to increase efficiency.

[0287] FIG. 9 is a framework (hereinafter referred to as “hydrogen fuel supply framework”) for functional blocks that perform a series of hydrogen fuel supply procedures that can employ a hydrogen fuel supply communication bidirectional process according to one embodiment of the present invention.

[0288] Referring to FIG. 9, the hydrogen fuel supply framework is composed of function blocks for each use case (UC), including a discovery and pairing function block (hereinafter simply referred to as 'UC1' or 'UC-1'), a communication security function block (UC2 or UC-2), a communication protocol negotiation function block (UC3 or UC-3), a fueling protocol negotiation function block (UC4 or UC-4), a fueling parameter negotiation function block (UC5 or UC-5), a safety check-in function block (UC6 or UC-6), a monitoring and control function block (UC7 or UC-7), a safety check-out function block (UC8 or UC-8), a termination function block (UC9 or UC-9), an error handling function block (UC10 or UC-10), and an emergency handling function. Blocks (UC11 or UC-11) can be equipped.

[0289] Each of the UC1 to UC11 function blocks may correspond to time-series steps (S401 to S411) as illustrated in Fig. 9. In this case, Fig. 9 may also be understood as an operational flowchart including time-series steps (S401 to S411).

[0290] UC10 and UC11 may be individually connected to UC3 to UC8 and configured to perform error handling and / or emergency handling in each use case.

[0291] The aforementioned use cases are functional blocks that collectively provide the entire hydrogen fueling procedure of a hydrogen fueling system in a consistent manner for safe and secure fueling communication. Vehicles and dispensers can sequentially execute each use case in a specific order to achieve the hydrogen fueling goal.

[0292] Additionally, after the dispenser nozzle is connected to the vehicle receptacle, the vehicle and the dispenser can perform fuel supply communication by implementing each use case in the order shown in Fig. 9. However, the vehicle and the dispenser may omit certain use cases as needed based on predefined requirements.

[0293] Each of the aforementioned use cases can be implemented through communication between a dispenser control system of a dispenser that supplies hydrogen as fuel to a hydrogen fuel vehicle according to a fueling protocol for a hydrogen fuel vehicle and the hydrogen fuel vehicle.

[0294] Meanwhile, the hydrogen fuel cell vehicle (hereinafter referred to simply as "vehicle") and dispenser implementing the aforementioned use case can exchange data for vehicle identification in UC-1. To this end, the vehicle may be equipped with sensors, an electric control unit (ECU), a transmitter, and a receiver. The receiver may be integrated with the transmitter for two-way communication.

[0295] The dispenser may be configured to receive specific data from the vehicle. The dispenser may store data for data logging or data designated by the charging station programmable logic controller (PLC) for use in the fueling protocol. Data logging may refer to a process of collecting data over a period of time to analyze a specific operating state of a hydrogen fueling system or to record data-based events / operations in a system or network environment, or data collected by this process. For two-way communication, the charging station may be equipped with a sensor designated by the fueling protocol, and the charging station PLC or electronic control unit may obtain measurements from the sensor and transmit these measurements to the vehicle. The aforementioned vehicle or charging station may use existing communication protocol standards for communication, such as infrared, Wi-Fi, or Bluetooth.

[0296] Additionally, the vehicle and dispenser can establish a communication channel between the vehicle and dispenser, physically connected at the vehicle-dispenser interface. The pairing process for establishing this communication channel can be performed using wired, optical, or wireless technologies.

[0297] The discovery and pairing procedure or pairing process may have prerequisites for the dispenser nozzle to be inserted into and securely connected to a vehicle fueling receptacle. The vehicle fueling receptacle may be simply referred to as a vehicle receptacle or receptacle.

[0298] Furthermore, the vehicle and dispenser inherently know which communication protocol to use. Therefore, subsequent communications after UC-1 can rely solely on the communication protocol agreed upon in the current use case, either as a discovery and pairing procedure or as a post-condition of the pairing process. If either the vehicle or dispenser selects a communication protocol outside the agreed upon scope, the selected communication will not occur. In other words, even if the pairing process completes successfully, fuel authorization or fuel delivery authorization may not be granted.

[0299] All methods used to pair the vehicle and dispenser must be designed to avoid increasing the risk of ignition or explosion beyond an acceptable level. For example, all wired pairing methods must be designed to mitigate or prevent sparking hazards caused by electrostatic discharge.

[0300] In terms of the effectiveness of physical pairing, any method used to pair a vehicle and a dispenser can be integrated into a vehicle-dispenser interface or installed so as to have proximity between the vehicle's fueling receptacle and the dispenser's nozzle and hose assembly. Here, the interface can refer to something physically integrated into the nozzle-receptacle interface. Proximity can be defined by hardware associated with the pairing method. For example, the physical geometry used for infrared communication can be specified, including the allowed distance between the transmitter and receiver. Furthermore, the physical configuration of the hydrogen fueling hardware can be pre-specified, and proximity does not include a pairing method that risks pairing a physically unconnected vehicle and dispenser, such as a relatively long-range wireless communication technology such as Bluetooth. Infrared communication can be referred to as infrared data association (IrDA) communication and can include bidirectional infrared (bi-IrDA) communication.

[0301] In steps S401, S403, S404, and / or S405, information on interoperability and compatibility between the mobility and the dispenser can be shared and mutually checked.

[0302] In steps S401, S403, S404, and / or S405, it can be determined whether the mobility and the dispenser each support bidirectional communication.

[0303] In steps S401, S403, S404, and / or S405, a communication protocol associated with the fuel supply protocol may be negotiated, and fuel supply parameters according to the fuel supply protocol may be negotiated. At this time, information regarding the interoperability and compatibility of the communication protocol and / or the fuel supply protocol may be shared in advance in S401, and information regarding preferences or priorities on the mobility or dispenser side may be shared.

[0304] Based on the information about interoperability, compatibility, preference, or priority shared in step S401, communication protocols, fueling protocols, and fueling parameters may be negotiated in steps S403, S404, and / or S405.

[0305] When there is a difference between the information shared in step S401 and the information identified in steps S403, S404, and / or S405 (e.g., when there is a change in the supportable level of two-way communication), the communication protocol, fueling protocol, and fueling parameters may be negotiated or renegotiated based on the updated interoperability, compatibility, preference, or priority in steps S403, S404, and / or S405, respectively.

[0306] Whether to allow control of the hydrogen fuel supply process, or control support / assistance, by the artificial neural network model described in FIGS. 1 to 8 can be determined by the fuel supply protocol, and whether to allow control of the hydrogen fuel supply process, or control support / assistance, by the artificial neural network model can be determined in at least one of steps S401, S403, S404, and / or S405 of FIG. 9.

[0307] Whether to allow control of the hydrogen fuel supply process by the model prediction control (MPC) described in FIGS. 1 to 8, or control support / assistance, can be determined by the fuel supply protocol, and whether to allow control of the hydrogen fuel supply process by the model prediction control, or control support / assistance, can be determined in at least one of steps S401, S403, S404, and / or S405 of FIG. 9.

[0308] The level of communication supported between the mobility and the dispenser can be represented by Tables 1 and 2 below.

[0309] Level Description: Non-Communication Level 0: When the vehicle or charging station does not have communication equipment, or when communication is impossible due to a malfunction. Communication Level 1: Communication is possible, but is not used for safety functions and can be used for data collection. If risk mitigation measures due to data defects are provided, it can be used to improve the control function of the fuel supply process. Level 2: Communication is possible, and static data is reliable, so it can be used directly for safety functions. Dynamic data is the same as Level 1. Communication (ANN model basic method). Level 3: Communication is possible, and both static and dynamic data are reliable, so it can be used directly for safety and control functions related to fuel supply.

[0310] Table 1 provides examples of communication reliability levels, graded from 0 to 3, based on various potential communication failures, failures, and data security / reliability issues. The communication levels in Table 1 can be referenced from the communication levels included in ISO 19885-1, which is currently under development.

[0311] Level of classification Simulation Basic information Charging conditions Relearning conditions Non-communication Level 0 Based on the total capacity of the vehicle storage tank and the basic information of the station, worst-case conditions are assumed. After checking the initial vehicle residual pressure and total capacity, charging is performed at a constant speed with the lowest flow rate as the worst-case conditions. Non-communication Analysis and selective reflection only of actual problem situations that occur during the charging process. Communication Level 1 Generate basic simulation conditions based on static data of vehicles and charging stations, but apply conservatively to individual static conditions, and apply conservative standards to dynamic conditions. Use the received dynamic data as a reference (for checking in case of an abnormal signal), and conservatively predict the temperature and pressure of the vehicle based on the temperature and pressure of the breakaway. Relearn based on conservatively operated dynamic data (breakaway standard), accumulate the received actual dynamic data, and derive correction factors through periodic comparative learning. Level 2 Apply the static data of vehicles and charging stations directly individually, but apply conservative standards to dynamic data. Use the received dynamic data as a reference (for checking in case of an abnormal signal), and conservatively predict the temperature and pressure of the vehicle based on the temperature and pressure of the breakaway. Use the conservatively operated dynamic data (breakaway standard) Baseline) Baseline relearning, accumulation of actual dynamic data received, and correction factor derivation through periodic comparative learning Level 3 Basic simulation using individual static and dynamic data of vehicles and charging stations Real-time control operation optimized for the situation based on real-time communication data All recorded fuel supply data base relearning

[0312] Table 2 shows the control levels for specifying static and dynamic variables, the allowable range for untrusted data, and alternative variables, depending on the level of communication reliability. While some functions are limited at lower communication levels, the fueling protocol operates at each level.

[0313] The hydrogen fuel supply protocol based on ANN-MPC proposed in one embodiment of the present invention corresponds to a situation corresponding to Level 3 defined in Tables 1 and 2, and it can be assumed that both static data and dynamic data transmitted and received with mobility (300) and charging station (200) are reliable and can be directly utilized.

[0314] Figure 10 is a flowchart illustrating a hydrogen fuel supply method according to one embodiment of the present invention.

[0315] According to one embodiment of the present invention, a method for fueling hydrogen in a hydrogen-fueled mobility may include a step (S1100) of performing a first hydrogen fueling process controlled based on a first fueling protocol; a step (S1200) of acquiring first fueling data obtained in the first hydrogen fueling process; and a step (S1300) of providing control information for a second hydrogen fueling process according to a first prediction cycle based on the first fueling data.

[0316] According to one embodiment of the present invention, a method for supplying hydrogen to hydrogen fuel mobility may further include a step (S1400) of performing a second hydrogen fuel supply process based on the control information and the first fuel supply protocol provided in step S1300.

[0317] In a method for supplying hydrogen to hydrogen fuel mobility according to one embodiment of the present invention, the first prediction period can be determined by user input.

[0318] In a method for supplying hydrogen to hydrogen fuel mobility according to one embodiment of the present invention, the first prediction period may be determined based on a) reliability of a model for generating control information, b) reliability of fuel supply data obtained by communication between the mobility and a dispenser for supplying hydrogen to the mobility, c) reliability of the mobility or the dispenser, or d) stability of climatic environmental conditions.

[0319] In a method for supplying hydrogen to hydrogen fuel mobility according to one embodiment of the present invention, control information can be generated by a model prediction control (MPC) operation.

[0320] In a method for supplying hydrogen to hydrogen fuel mobility according to one embodiment of the present invention, control information can be generated by an artificial neural network (ANN) model.

[0321] According to one embodiment of the present invention, a method for supplying hydrogen to a hydrogen fuel mobility system may further include a step of determining a second prediction cycle for predicting a third hydrogen fueling process following a second hydrogen fueling process. That is, in one embodiment of the present invention, the prediction cycle may be dynamically changed.

[0322] At this time, the method for supplying hydrogen to hydrogen fuel mobility according to one embodiment of the present invention may further include a step of obtaining second fuel supply data obtained in a second hydrogen fuel supply process.

[0323] At this time, the step of determining the second prediction cycle may determine the second prediction cycle based on the predicted state information during the process of generating the second fuel supply data and the control information for the second hydrogen fuel supply process. That is, in one embodiment of the present invention, when the prediction time cycle is dynamically changed, the prediction time cycle (prediction cycle) may be dynamically changed based on the difference between the predicted value and the actual value.

[0324] According to one embodiment of the present invention, a method for supplying hydrogen to hydrogen fuel mobility may further include a step (S401, S403, S404, S405) of determining whether a first fuel supply protocol supports a process of generating control information by model prediction control (MPC).

[0325] According to one embodiment of the present invention, a method for supplying hydrogen to hydrogen fuel mobility may further include a step (S401, S403, S404, S405) of determining whether a communication protocol associated with the first fuel supply protocol supports two-way communication between the mobility and a dispenser that supplies hydrogen to the mobility.

[0326] According to one embodiment of the present invention, a method for supplying hydrogen to hydrogen fuel mobility may further include a step (S401, S403, S404, S405) of determining whether the mobility and a dispenser supplying hydrogen to the mobility support two-way communication between the mobility and the dispenser.

[0327] The first hydrogen fuel supply process may refer to any nth sequence or control / prediction horizon with reference to FIGS. 11 and 12 described below.

[0328] The second hydrogen fuel supply process may refer to any (n+1)th sequence or control / prediction horizon with reference to FIGS. 11 and 12 described below.

[0329] For convenience of explanation, FIGS. 11 and 12 assume that a fuel supply protocol supporting ANN-MPC operation used in one embodiment of the present invention is adopted.

[0330] The most important component in control using MPC is the prediction model. The higher the accuracy of the prediction model, the more accurate the control can be. ANN-MPC can refer to a case where an artificial neural network model (120) is used as the prediction model and MPC is used as the control method.

[0331] The basic platform that constitutes ANN-MPC uses the temperature, pressure, and outside temperature of mobility (300) and charging station (200) as inputs, as shown in Fig. 3, and sets the temperature and pressure of the mobility (300) storage tank as outputs. By utilizing a large amount of theoretical and actual data, the relationship between input and output can be learned, thereby improving the accuracy of the correlation.

[0332] The operation of the ANN-MPC protocol, as illustrated in Fig. 4, regards the charging station as a type of plant, sets the fuel supply speed control as a goal, uses ANN as a prediction model, and calculates appropriate process inputs by optimizing the ANN's prediction and objective function through the optimization process, thereby controlling the fuel supply speed, Pressure ramp rate (PRR).

[0333] An artificial neural network model can be trained using simulated values ​​related to the correlation between temperature and pressure changes in mobility storage tanks depending on the temperature and pressure of hydrogen fluid fuel injected into mobility at charging stations and actual measured data from mobility, dispensers, and charging stations.

[0334] The reliability of the artificial neural network model (120) can be improved by continuously adding data obtained under various conditions and repeating the training. The artificial neural network model (120) can be used as a predictive model and the MPC can be configured as a controller to train the ANN-MPC model to achieve optimization of hydrogen fueling time for mobility.

[0335] To address the known issues in applying the ANN-MPC model to field hydrogen fuel supply, the following may be required:

[0336] Since the equipment specifications of a charging station and the specifications of a mobility storage tank vary greatly depending on the type of device, a specialized artificial neural network model (120) that individually reflects the characteristics of the mobility model and the charging station may be required.

[0337] In a situation where MPC is applied to control a specialized artificial neural network model (120) implemented for each type of individual mobility / charging station device, individual and selective fuel supply speed control suitable for each situation may be required.

[0338] Since there may be differences in equipment specifications or constraints, etc., in applying the same fuel supply rule in all individual situations, an individual setting control function for the fuel supply result prediction range based on the reliability of the artificial neural network model (120) may be required at the end user stage.

[0339] FIG. 11 is a diagram showing an example of a prediction time cycle for implementing the embodiment of FIG. 10 on a time axis.

[0340] Referring to Fig. 11, the operation performed in the time domain in the hydrogen fuel supply control process using the ANN-MPC model is illustrated.

[0341] The ANN-MPC model can predict future outcomes after a given time based on data from charging stations / dispensers and mobility at a given time, and determine the fuel delivery rate (PRR) based on the predicted values.

[0342] The prediction time cycle of the ANN-MPC model can be fixed and operated depending on the amount and type of data acquired, external conditions, etc. The prediction time cycle can have values ​​such as 10 seconds or 15 seconds. The ANN-MPC model can operate in a way that reduces the deviation between the predicted value and the actual value for measured fuel supply data.

[0343] For example, if a prediction period of 10 seconds is set, the ANN-MPC model can predict the situation 10 seconds later based on the actual data input at t=0 seconds, determine the fuel supply speed based on the predicted result, input the actual data again 10 seconds later, compare the predicted value with the actual value to reduce the deviation, and then predict the result value 10 seconds later based on the actual value.

[0344] In one embodiment of the present invention, the prediction period can be set to a fixed value in the ANN-MPC model through evaluation at the development stage and operated.

[0345] In another embodiment of the present invention, when specialized artificial neural network models (120) are individually implemented for each type of mobility and charging station device, an additional adjustment function for the prediction cycle may be required.

[0346] In FIG. 11, the first cycle may correspond to the first hydrogen fuel supply process, and the second cycle may correspond to the second hydrogen fuel supply process.

[0347] During the first cycle, the first hydrogen fuel supply process is performed (S1100), and at time t=10, which is during the first cycle or immediately after the first cycle, the first fuel supply data (T CHSS, Real , P CHSS, Real ) can be obtained (S1200).

[0348] In Fig. 11, for convenience of explanation, the first prediction period is assumed to be 10 seconds. According to the first prediction period, the ANN-MPC model predicts data (T) at t=20 hours after the first prediction period by MPC operation. CHSS, Predicted , P CHSS, Predicted ) can be obtained. The controller of the dispenser using the ANN-MPC model can generate a fuel supply rate (PRR) for a second hydrogen fuel supply process corresponding to the second cycle as a control command, which is generated based on the prediction data at t=20 when the second cycle ends and the first fuel supply data at t=10 hours when the first cycle ends.

[0349] At this time, the ANN-MPC model can provide control information for the next cycle to the dispenser controller based on the predicted data for the next cycle and the actual data of the current time (S1300).

[0350] The controller of the dispenser can perform the second hydrogen fuel supply process according to a control command (fuel supply rate (PRR)) reflecting control information (S1400).

[0351] Similarly, based on the actual data after the second cycle and the predicted data predicted after the third cycle, the ANN-MPC model and / or controller can generate control information and / or control commands for the third cycle and perform the third hydrogen fueling process.

[0352] FIG. 12 is a conceptual diagram illustrating an example of a platform on which a hydrogen fuel supply process for implementing the embodiment of FIG. 10 is performed.

[0353] Referring to FIG. 12, a conceptual diagram illustrating a method for optimizing operation of a hydrogen fuel supply protocol through control of a predicted time cycle (Y) of an artificial neural network model (120) as a hydrogen fuel supply control process using an ANN-MPC model according to one embodiment of the present invention.

[0354] The artificial neural network model (120) can predict the result value after the prediction period (X+Y(j+1))) based on the current point in time (X+Yj) based on real-time data obtained by the step (S1200) according to the prediction period (Y), and provide control information on the fuel supply speed using the predicted value (122) (S1300). (j=0, 1, 2, etc.)

[0355] The controller (110) generates a control command for a fuel supply speed (PRR or m_dot) based on control information, and can perform the first and second fuel supply processes by executing the control command (S1100, S1400).

[0356] During the development phase of the artificial neural network model (120), the prediction time period (Y) may be set to a short period to ensure initial reliability. As the development and learning of the artificial neural network model (120) mature and prediction accuracy improves, the prediction time period (Y) may be set to gradually longer periods.

[0357] The predicted time cycle (Y) can also be directly related to the cycle for controlling and changing the actual pressure rise rate. Since the temperature and pressure of the charging station (200) and mobility (300) do not respond immediately to control within a few seconds, the pressure rise rate control change cycle can generally be controlled at intervals of at least 5 seconds.

[0358] To improve fuel delivery performance (e.g., shortening full-charge times), frequent control operations with short prediction intervals may be advantageous. However, from an operational efficiency perspective, frequent control operations can increase system load, leading to increased maintenance costs and failure rates. Therefore, minimizing flow control using long prediction intervals is advantageous for operational efficiency, while improving fuel delivery performance is advantageous with short prediction intervals. Therefore, an efficient prediction interval setting may be required through a trade-off between fuel delivery performance and operational efficiency (stability).

[0359] The following can be referred to as representative factors that affect the determination of the prediction period (Y) of the artificial neural network model (120).

[0360] a) Reliability of the artificial neural network model (120)

[0361] As the artificial neural network model (120) is operated with the initial model installed in the charging station (200), the reliability of the artificial neural network model (120) can gradually improve as learning and re-learning are performed using actual fuel supply results as data.

[0362] The reliability of individual artificial neural network models (120) may differ depending on the start time of operation of the artificial neural network model (120), the frequency of fuel supply, the characteristics of the charging station (200), etc., and as the reliability is lower, correction of the artificial neural network model (120) may be required through input of predicted values ​​and actual values ​​through a short prediction cycle. In this case, the reliability of the artificial neural network model (120) may be improved by securing data according to the increase in the number of fuel supplies and retraining through the data.

[0363] The initial model of the artificial neural network model (120) is trained only with theoretical simulations based on conservative boundary conditions to reflect various conditions, and thus may differ somewhat from actual field data. If the prediction cycle of the artificial neural network model (120), which is initialized when the temperature of the storage tank of the current mobility (300) is 20 degrees and 100 atm, is set to 60 seconds and the temperature and pressure after 60 seconds are predicted to be 30 degrees and 200 atm, it can be assumed that the fuel supply speed is controlled with a PRR of 100 atm / 1 minute.

[0364] At this time, if the actual value and the predicted value after 60 seconds are different, the fuel supply speed is controlled to change. However, the gap between the actual value and the predicted value may be larger as the training level of the artificial neural network model (120) is lower. As the training level, maturity, and / or reliability of the artificial neural network model (120) are low and the gap between the actual value and the predicted value is larger, the change in the fuel supply speed control command is larger. This change in the control command may again cause an increase in error and an increase in the control burden of the charging station (200).

[0365] Therefore, for the initial model, setting a relatively short prediction period, for example, 10 or 20 seconds, can reduce the difference between the predicted value and the actual value, retrain the data on which fuel supply has been performed to improve reliability, and as the reliability of the artificial neural network model (120), i.e., the ANN-MPC model, increases, the prediction period can be gradually increased.

[0366] b) Reliability of real-time two-way reception data

[0367] As previously disclosed in Table 1 and Table 2, data received from mobility (300) and charging station (200) are determined according to the applicable communication procedures and regulations, and may exhibit differences in reliability.

[0368] If the reliability level of the transmitted data is low, it may be necessary to keep the prediction cycle as short as possible to counteract inaccurate data acquisition.

[0369] The ANN-MPC model can optimize speed through control based on real-time communication data. If the reliability of the received communication data is guaranteed, the predicted cycle period can be set long. However, if the received data may contain noise or errors periodically or sporadically, long-term cycle predictions based on incorrect real-time data may cause safety issues due to incorrect control. In addition, the gap between the actual values ​​when normal data is received in the next cycle may cause abrupt changes in the fuel supply speed control command, which may increase the control burden on the charging station (200) and / or the dispenser (100).

[0370] c) Reliability of equipment

[0371] In cases where facility stability is insufficient in MPC control using an artificial neural network model (120), a shorter prediction cycle may be required. For example, if the responsiveness of ANN-MPC control is degraded, or if the response differs from normal operation due to design errors or deficiencies in the facility, or construction issues, frequent prediction cycle control may be required to correct the control.

[0372] That is, in cases where the reliability of the equipment of the mobility (300), charging station (200), and / or dispenser (100) is low, frequent prediction cycle control may be required to ensure safety.

[0373] The artificial neural network model (120) can predict the result value after the prediction period in advance according to the prediction period, and provide control information on the fuel supply speed using the predicted value (122) (S1300).

[0374] The values ​​learned by the artificial neural network model (120) are information indicating the relationship between the output value and the input value, and the relationship between the input value and the output value is expected to follow general thermodynamic relationships. At this time, special responses and result values ​​that may affect the control process may appear due to design defects such as the response performance of the flow control valve of the charging station (200), insulation problems of the pipe, equipment capacity, and friction. In particular, when a new design structure is applied, there is a problem in construction, or the equipment or parts used do not meet the standards, a short time cycle may be required in the initial stage even if the control burden of the charging station (200) increases due to a short control time cycle.

[0375] d) Climatic and environmental stability

[0376] For environments that violate generally assumed heat exchange conditions with the outside air, control of the prediction cycle during initial model operation may be required. In situations where high or low temperatures, local temperature differences, strong winds, gusts, typhoons, or high humidity have a significant impact, control compensation through prediction cycle control may be required to improve reliability.

[0377] The artificial neural network model (120) can start from a basic model created based on simulation results that set various climatic and environmental factors as boundary conditions. While fuel supply data is generated through a fuel supply process utilizing the basic model and then retrained to increase reliability, the climatic and environmental factors set in the initial simulation stage may be diluted as learning progresses. If an artificial neural network model (120) trained in a cold region is re-operated in a hot region with very different climatic and environmental conditions, reliability may need to be re-established through a short cycle evaluation.

[0378] Referring again to FIG. 12, a conceptual structure of a system that performs a method for optimizing hydrogen fuel supply protocol operation through prediction time cycle control of an artificial neural network model (120) of an ANN-MPC model according to one embodiment of the present invention is illustrated.

[0379] In another embodiment of the present invention referring to FIG. 12, an embodiment may be disclosed in which a user determines and inputs a prediction period (Y).

[0380] The fuel supply process between the hydrogen charging station (200) and hydrogen mobility is handled by the dispenser (100), and the user can perform variable setting and control operations related to fuel supply through the dispenser (100).

[0381] The predicted cycle time setting suitable for the operating environment of the charging station (200) can be input by the charging station (200) operator through the control panel of the dispenser (100) according to the guidelines presented for each situation. The user input value is transmitted to the artificial neural network model (120) to predict the result value after the input time and set the fuel supply speed, and the controller (110) can control the hydrogen fuel supply process using the control information based on the predicted value (122) obtained through the MPC. As a result of the control, the fuel supply speed is determined, and hydrogen can be injected from the hydrogen charging station (200) through the dispenser (100) to the hydrogen mobility (300).

[0382] The actual value after the input cycle time is transmitted from the hydrogen charging station (200) and hydrogen mobility (300) to the dispenser (100), and based on the actual value, the future value (X+Y(j+1))) for the input time (Y) from the current time (X+Yj) is predicted, and the controller (110) can change and control the fuel supply speed. (j=0, 1, 2, etc.)

[0383] The higher the reliability of the artificial neural network model (120), the more likely it is that a result value after a long period of time can be predicted, and the higher the reliability, the less likely the difference between the predicted time and the actual measured value per cycle is, so that abrupt fuel supply speed control can be reduced.

[0384] At the start of fuel supply, the MPC operation and control process is performed based on the predicted cycle (Y) input by the user, and thereafter, the MPC operation and control process can be repeatedly performed according to the given predicted cycle (Y).

[0385] In the training stage of the artificial neural network model (120), the reliability of the model can be improved by learning theoretical and actual data that can reflect various situations, controlling the fuel supply speed by predicting a certain amount of time later, and correcting the deviation between the predicted value (122) and the actual value by comparing it with the measured value after the actual execution time.

[0386] The reliability of the artificial neural network model (120) can be improved as the absolute amount of data to be learned and the quality of data that can reflect various situations are improved. The initial artificial neural network model (120) has a difference between the predicted value (122) and the measured value, and theoretically, the longer the prediction period (Y), the greater the difference between the predicted value (122) and the measured value. Therefore, in the initial model with a small amount of learning data, the prediction period (Y) is set short, and as learning progresses and the reliability improves, the prediction period (Y) can be set longer.

[0387] Although an embodiment of setting the prediction period (Y) differently in the ANN-MPC operation learning process of the artificial neural network model (120) is illustrated, another embodiment of the present invention may also disclose an embodiment of dynamically changing the prediction period (Y) in the hydrogen fuel supply process.

[0388] At this time, when the difference between the predicted value (122) and the actual value in the predicted cycle (Y) set at the beginning of fuel supply stabilizes within a predetermined threshold value, the predicted cycle (Y) can be gradually changed to a longer value.

[0389] The hydrogen fuel delivered from the dispenser (100) to the mobility (300) may be gaseous or liquid, and can generally be controlled at a single speed or variable speed. APRR (Average pressure ramp rate) is a method of controlling to target the same pressure increase speed from the beginning to the end of the fuel supply, and PRR (pressure ramp rate) is a method of controlling by changing the pressure increase speed as needed during the fuel supply process. The pressure increase speed is also expressed as m_dot. In order to control the pressure of the mobility (300) storage tank to increase at a constant speed or a desired speed, the dispenser (100) controls the pressure increase speed by adjusting the degree of opening and closing of the flow control valve installed in the pipe through which the hydrogen gas moves, and the mass flow rate can be measured at this time. The fuel supply speed m_dot recorded as mass flow rate and APRR and PRR are closely related, and when the mass flow rate increases, the pressure increase speed increases, but not necessarily in direct proportion.

[0390] The artificial neural network model (120) of the embodiment of the present invention described above can learn the function of controlling the hydrogen fuel supply process or learn a part of the process of the control function using simulation data and real on-site data.

[0391] The actual field data used for learning may vary depending on the specifications of the storage tank or equipment on the charging station (200) side, and if fuel is actually supplied for a specific mobility (300), the reliability of the artificial neural network model (120) may be reduced if the hardware specifications at the time of learning data and actual application are different.

[0392] In order to improve and optimize the reliability and efficiency of the artificial neural network model (120), individual models specialized for each type and detailed specifications of the charging station (200) and mobility (300) can be learned.

[0393] Conditions for classifying the sub-individual models may include, for the charging station (200), maximum supply pressure, precooling temperature, maximum flow rate, etc.

[0394] In the case of mobility (300), it may include a vehicle model name, CHSS total volume, largest container volume, allowable maximum flow rate, etc., and the vehicle model name may include a serial number classified by manufacturer, etc.

[0395] At this time, the CHSS of the mobility (300) can be implemented by including various types of storage tanks of different sizes for safety, and in the mobility (300), not only the total volume of the CHSS but also the specifications of the detailed storage tanks can be considered as important.

[0396] An artificial neural network model (120) that is individually specialized and learned can be placed in a dispenser (100) and operated, and depending on the embodiment, can be stored in a mobility (300) or a third-party location and operated by communicating via a network.

[0397] A basic artificial neural network model is provided in the dispenser (100), and subordinate artificial neural network models that are learned and differentiated according to individual specialized conditions can be deployed and managed in the dispenser (100). The basic artificial neural network model can be initialized by the manufacturing company of the mobility (300).

[0398] In some embodiments, even if the basic artificial neural network model is stored in the mobility (300) or third-party location, the individual model for hydrogen fuel supply may be stored and operated on the dispenser (100) side when operated in an actual field.

[0399] In another embodiment of the present invention, a manufacturer of a mobility (300) can create and distribute an initialized basic artificial neural network model using data specific to the characteristics of a storage tank. The basic artificial neural network model can be stored in storage within the mobility (300) and transmitted to the dispenser (100) after the mobility (300) enters a charging station (100).

[0400] Differentiation of specialized artificial neural network models and learning of lower-level individual models can be primarily performed by the dispenser (100). Lower-level individual models can be learned or relearned based on actual fuel supply data performed by the dispenser (100).

[0401] To ensure the reliability of individual sub-models, retraining of the artificial neural network model can be performed when sufficient field data has been accumulated. This process can be performed periodically or aperiodically in the dispenser (100).

[0402] To ensure the reliability of the individual sub-models, the mobility (300) manufacturing company can collect fuel supply data received from the charging station (200) and retrain the basic artificial neural network model. The basic artificial neural network model updated through retraining (which can be interpreted as a model specialized for each mobility (300)) can be transmitted to the dispenser (100) for updating. The basic artificial neural network model can be transmitted to the dispenser (100) when the mobility (300) enters the charging station (200).

[0403] A third party can be operated to ensure the reliability of individual sub-models and provide integrated management.

[0404] Third parties may be government agencies, associations, professional management companies, etc.

[0405] An association may be a technology-related association or a standards-related association.

[0406] Fuel supply data generated in actual fields can be collected through cloud servers and retrained using an artificial neural network model. Third parties can create and distribute basic artificial neural network models on behalf of mobility (300) companies.

[0407] Additionally, subordinate individual models can be collected periodically or aperiodically, comprehensively upgraded, and the upgraded individual models can be provided back to individual gas stations (200). This process can also be implemented as a type of federated learning.

[0408] FIG. 13 is a conceptual diagram illustrating an example of a generalized hydrogen fuel supply control device, hydrogen fuel supply control system, hydrogen fuel supply simulation device, hydrogen fuel supply simulation system, hydrogen fuel supply test platform, hydrogen fuel supply test system, or computing system capable of performing at least a portion of the processes of FIGS. 1 to 12.

[0409] Referring to FIG. 13, a computing system (3000) according to one embodiment of the present invention may be configured to include a processor (3100), a memory (3200), a communication interface (3300), a storage device (3400), an input interface (3500), an output interface (3600), and a bus (3700).

[0410] A computing system (3000) according to one embodiment of the present invention may include at least one processor (3100) and a memory (3200) that stores instructions that instruct the at least one processor (3100) to perform at least one step. At least some steps of a method according to one embodiment of the present invention may be performed by the at least one processor (3100) loading and executing instructions from the memory (3200).

[0411] The processor (3100) may mean 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.

[0412] Each of the memory (3200) and the storage device (3400) may be configured with at least one of a volatile storage medium and a non-volatile storage medium. For example, the memory (3200) may be configured with at least one of a read-only memory (ROM) and a random access memory (RAM).

[0413] Additionally, the computing system (3000) may include a communication interface (3300) that performs communication via a wireless network.

[0414] Additionally, the computing system (3000) may further include a storage device (3400), an input interface (3500), an output interface (3600), etc.

[0415] Additionally, each component included in the computing system (3000) can communicate with each other by being connected by a bus (3700).

[0416] Examples of the computing system (3000) of the present invention may include a communicable desktop computer, a laptop computer, a notebook, a smart phone, 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 PDA (Personal Digital Assistant), etc.

[0417] A device mounted on a hydrogen-fueled mobility (300) according to one embodiment of the present invention and controlling a process of supplying hydrogen fuel may include a memory (3200) storing at least one command; and a processor (3100) executing at least one command.

[0418] At this time, the processor (3100) can perform a first hydrogen fuel supply process controlled based on a first fuel supply protocol by at least one command, acquire first fuel supply data obtained in the first hydrogen fuel supply process, and provide control information for a second hydrogen fuel supply process according to a first prediction cycle based on the first fuel supply data.

[0419] In a device for controlling a process of supplying hydrogen to a hydrogen-fueled mobility according to one embodiment of the present invention, a first prediction period can be determined by a user input.

[0420] In a device for controlling a process of supplying hydrogen to a hydrogen-fueled mobility according to one embodiment of the present invention, a first prediction period may be determined based on the reliability of a model for generating control information, the reliability of fuel supply data obtained through communication between the mobility and a dispenser that supplies hydrogen to the mobility, the reliability of the mobility or the dispenser, or the stability of climate environment conditions.

[0421] In a device for controlling a process of supplying hydrogen to a hydrogen-fueled mobility according to one embodiment of the present invention, control information may be generated by a model prediction control (MPC) operation.

[0422] In a device for controlling a process of supplying hydrogen to a hydrogen-fueled mobility according to one embodiment of the present invention, control information may be generated by an artificial neural network (ANN) model.

[0423] In a device for controlling a process of supplying hydrogen fuel to a hydrogen-fueled mobility according to one embodiment of the present invention, the processor may determine a second prediction cycle for predicting a third hydrogen fueling process following a second hydrogen fueling process.

[0424] At this time, the processor (3100) can obtain second fuel supply data obtained during the second hydrogen fuel supply process.

[0425] At this time, the processor (3100) may determine the second prediction cycle based on the predicted state information in the process of generating the second fuel supply data and control information for the second hydrogen fuel supply process when determining the second prediction cycle.

[0426] In a device for controlling a process of supplying hydrogen to a hydrogen-fueled mobility according to one embodiment of the present invention, a processor (3100) can determine whether a first fuel supply protocol supports a process of generating control information by model prediction control (MPC).

[0427] In a device for controlling a process of supplying hydrogen to a hydrogen-fueled mobility according to one embodiment of the present invention, a processor (3100) can determine whether a communication protocol associated with a first fuel supply protocol supports bidirectional communication between the mobility and a dispenser that supplies hydrogen to the mobility.

[0428] In a device for controlling a process of supplying hydrogen to a hydrogen-fueled mobility according to one embodiment of the present invention, a processor (3100) can determine whether the mobility and a dispenser that supplies hydrogen to the mobility support two-way communication between the mobility and the dispenser.

[0429] A control device for a hydrogen fuel supply process according to one embodiment of the present invention may be placed in a dispenser that supplies hydrogen to a mobility vehicle, or may be implemented as a controller that is not placed in the dispenser but is capable of electronically communicating with the dispenser and influencing the fueling of the dispenser.

[0430] The operations of the method according to an embodiment of the present invention can be implemented as a computer-readable program or code on a computer-readable recording medium. A computer-readable recording medium includes any type of recording device that stores information readable by a computer system. Furthermore, a computer-readable recording medium can be distributed across network-connected computer systems, allowing the computer-readable program or code to be stored and executed in a distributed manner.

[0431] Additionally, the computer-readable recording medium may include hardware devices specifically configured to store and execute program instructions, such as ROM, RAM, flash memory, etc. The program instructions may include not only machine language codes produced by a compiler, but also high-level language codes that can be executed by a computer using an interpreter, etc.

[0432] While some aspects of the present invention have been described in the context of a device, they may also represent a description of a corresponding method, wherein a block or device corresponds to a method step or a feature of a method step. Similarly, aspects described in the context of a method may also be described as a corresponding block or item or a feature of a corresponding device. 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 an electronic circuit. In some embodiments, at least one or more of the most important method steps may be performed by such a device.

[0433] In 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 embodiments, the field-programmable gate array may operate in conjunction with a microprocessor to perform one of the methods described herein. In general, the methods are preferably performed by some hardware device.

[0434] Although the present invention has been described above with reference to preferred embodiments thereof, it will be understood by those skilled in the art that various modifications and changes may be made to the present invention without departing from the spirit and scope of the present invention as set forth in the claims below.

Claims

1. A method for supplying hydrogen to a mobility vehicle that uses hydrogen as a fuel, A step of performing a first hydrogen fuel supply process controlled based on a first fuel supply protocol; A step of obtaining first fuel supply data obtained in the first hydrogen fuel supply process; and A step of providing control information for a second hydrogen fuel supply process according to a first prediction cycle based on the first fuel supply data; Including, A method for fueling hydrogen for hydrogen fuel mobility.

2. In paragraph 1, The above first prediction period is determined by user input, A method for fueling hydrogen for hydrogen fuel mobility.

3. In paragraph 1, The first prediction period is determined based on the reliability of the model generating the control information, the reliability of the fuel supply data obtained through communication between the mobility and the dispenser that supplies hydrogen to the mobility, the reliability of the mobility or the dispenser, or the stability of climate environment conditions. A method for fueling hydrogen for hydrogen fuel mobility.

4. In paragraph 1, The above control information is generated by the Model Prediction Control (MPC) operation. A method for fueling hydrogen for hydrogen fuel mobility.

5. In paragraph 1, The above control information is generated by an artificial neural network (ANN) model. A method for fueling hydrogen for hydrogen fuel mobility.

6. In paragraph 1, A step of determining a second prediction cycle for predicting a third hydrogen fuel supply process after the second hydrogen fuel supply process; Including more, A method for fueling hydrogen for hydrogen fuel mobility.

7. In paragraph 6, A step of obtaining second fuel supply data obtained in the second hydrogen fuel supply process; further comprising; The step of determining the second prediction period is: In the process of generating the second fuel supply data and the control information for the second hydrogen fuel supply process, the second predicted period is determined based on the predicted state information. A method for fueling hydrogen for hydrogen fuel mobility.

8. In paragraph 1, A step of determining whether the first fuel supply protocol supports a process of generating the control information by model prediction control (MPC); Including more, A method for fueling hydrogen for hydrogen fuel mobility.

9. In paragraph 1, A step of determining whether a communication protocol associated with the first fuel supply protocol supports bidirectional communication between the mobility and a dispenser that supplies hydrogen to the mobility; Including more, A method for fueling hydrogen for hydrogen fuel mobility.

10. In paragraph 1, A step of determining whether the mobility and the dispenser that supplies hydrogen to the mobility support two-way communication between the mobility and the dispenser; Including more, A method for fueling hydrogen for hydrogen fuel mobility.

11. A device that controls the process of supplying hydrogen to a hydrogen-fueled mobility vehicle. a memory storing at least one command; and A processor for executing at least one of the above instructions; Including, The processor, by means of at least one instruction, Performing a first hydrogen fuel supply process controlled based on the first fuel supply protocol, Obtaining first fuel supply data obtained in the above first hydrogen fuel supply process, Based on the first fuel supply data, control information for the second hydrogen fuel supply process is provided according to the first prediction cycle. A control device for the process of supplying hydrogen to hydrogen fuel mobility.

12. In paragraph 11, The above first prediction period is determined by user input, A control device for the process of supplying hydrogen to hydrogen fuel mobility.

13. In paragraph 11, The first prediction period is determined based on the reliability of the model generating the control information, the reliability of the fuel supply data obtained through communication between the mobility and the dispenser that supplies hydrogen to the mobility, the reliability of the mobility or the dispenser, or the stability of climate environment conditions. A control device for the process of supplying hydrogen to hydrogen fuel mobility.

14. In paragraph 11, The above control information is generated by the Model Prediction Control (MPC) operation. A control device for the process of supplying hydrogen to hydrogen fuel mobility.

15. In paragraph 11, The above control information is generated by an artificial neural network (ANN) model. A control device for the process of supplying hydrogen to hydrogen fuel mobility.

16. In paragraph 11, The above processor determines a second prediction cycle for predicting a third hydrogen fuel supply process after the second hydrogen fuel supply process. A control device for the process of supplying hydrogen to hydrogen fuel mobility.

17. In paragraph 16, The above processor obtains second fuel supply data obtained in the second hydrogen fuel supply process, When the above processor determines the second prediction period, In the process of generating the second fuel supply data and the control information for the second hydrogen fuel supply process, the second predicted period is determined based on the predicted state information. A control device for the process of supplying hydrogen to hydrogen fuel mobility.

18. In paragraph 11, The above processor determines whether the first fuel supply protocol supports a process of generating the control information by model prediction control (MPC). A control device for the process of supplying hydrogen to hydrogen fuel mobility.

19. In paragraph 11, The processor determines whether the communication protocol associated with the first fueling protocol supports bidirectional communication between the mobility and a dispenser that fuels the mobility with hydrogen. A control device for the process of supplying hydrogen to hydrogen fuel mobility.

20. In paragraph 11, The above processor determines whether the mobility and the dispenser that supplies hydrogen to the mobility support bidirectional communication between the mobility and the dispenser. A control device for the process of supplying hydrogen to hydrogen fuel mobility.

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