Device for controlling vehicle mounted with ammonia fuel battery and operation method thereof

Abstract

To provide a device and an operation method thereof for controlling a vehicle mounted with an ammonia fuel cell, which can minimize residual ammonia by stabilizing an ammonia decomposition rate of a cracker to contribute to environments and further produce clean energy.SOLUTION: A device 100 includes: an ammonia tank storing ammonia therein; a cracker which decomposes ammonia supplied from the ammonia tank to hydrogen; and a controller which calculates an estimated hydrogen consumption on the basis of at least one of real-time operation information and operation pattern information and controls at least one of a supply amount of ammonia and an internal condition of the cracker on the basis of the estimated hydrogen consumption.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present disclosure relates to an apparatus and method of operation for controlling a vehicle equipped with an ammonia fuel cell. [Background technology]

[0002] In the case of a motor vehicle, it includes a drive means capable of moving the vehicle by being supplied with energy by an energy source. One of the most widely used energy sources is the internal combustion engine, which runs on a fuel. However, since the combustion of fuel produces carbon dioxide, which pollutes the atmosphere, it is desirable to use less polluting energy sources.

[0003] The use of fuel cells, for example hydrogen fuel cells, to replace internal combustion engines is already well known. When hydrogen in the form of dihydrogen is oxidized by the fuel cell, the cell produces electricity and generates heat to power the drive means. Hydrogen can be stored in the form of gaseous ammonia, which is absorbed or adsorbed on salts in a storage cartridge. This is a sure way of storing hydrogen. Therefore, ammonia needs to be desorbed from the salt and then cracked to form dihydrogen that can be fed to the fuel cell. The terms absorption and desorption are used to refer to the storage and release of gaseous ammonia in or from the salt, respectively, as long as such storage is performed by absorption or adsorption.

[0004] When a vehicle equipped with an ammonia fuel cell is running, it is necessary to stabilize the ammonia decomposition rate of the cracker and minimize residual ammonia. To this end, research is being conducted into controlling the ammonia fuel cell system by monitoring minute changes in driving patterns based on real-time driving information and information on the driver's previous driving patterns. Summary of the Invention [Problem to be solved by the invention]

[0005] According to one aspect of the present disclosure, an apparatus for controlling a vehicle equipped with an ammonia fuel cell capable of stabilizing an ammonia decomposition rate of a cracker and minimizing residual ammonia by monitoring minute changes in driving patterns based on real-time driving information, information on a driver's existing driving patterns, and the like, and controlling an ammonia fuel cell system, and a method for operating the same can be provided, which can contribute to the environment and produce clean energy. [Means for solving the problem]

[0006] An apparatus for controlling a vehicle equipped with an ammonia fuel cell according to an embodiment of the present disclosure may include an ammonia tank for storing ammonia, a cracker for decomposing the ammonia supplied from the ammonia tank into hydrogen, and a controller for calculating a predicted hydrogen consumption amount based on at least one of real-time operation information and driving pattern information, and for controlling at least one of the ammonia supply amount and the internal conditions of the cracker based on the predicted hydrogen consumption amount.

[0007] According to one embodiment, the controller may include an expected hydrogen consumption calculation unit that calculates a plurality of expected hydrogen consumption amounts at a specific time point based on at least one of the real-time operation information and the driving pattern information for each specific time point, and calculates a final expected hydrogen consumption amount based on at least one of the plurality of expected hydrogen consumption amounts, and an ammonia supply amount control unit that calculates the ammonia supply amount required to ensure the final expected hydrogen consumption amount, and controls a piping and valve system so that the ammonia can be supplied to the cracker in the calculated supply amount so that the ammonia is supplied to the cracker in the amount of the ammonia supply amount.

[0008] According to one embodiment, the controller may include an expected hydrogen consumption calculation unit that calculates a plurality of expected hydrogen consumption amounts at a specific time point based on at least one of the real-time operation information and the driving pattern information for each specific time point, and calculates a final expected hydrogen consumption amount based on at least one of the plurality of expected hydrogen consumption amounts, and a cracker condition control unit that controls internal conditions of the cracker based on the amount of ammonia decomposed into hydrogen in the cracker and the final expected hydrogen consumption amount.

[0009] According to one embodiment, the controller may include an expected hydrogen consumption calculation unit that calculates a plurality of expected hydrogen consumption amounts at a specific time point based on at least one of the real-time operation information and the driving pattern information for each specific time point, and calculates a final expected hydrogen consumption amount based on at least one of the plurality of expected hydrogen consumption amounts; an ammonia supply amount control unit that calculates the amount of ammonia supply required to secure the final expected hydrogen consumption amount, and controls a piping and valve system so that the ammonia is supplied to the cracker in the calculated supply amount so that the ammonia is supplied to the cracker in the amount of the ammonia supply; and a cracker condition control unit that sets internal conditions of the cracker such that the amount of decomposition of the ammonia into the hydrogen in the cracker reaches the final expected hydrogen consumption amount.

[0010] According to one embodiment, the expected hydrogen consumption calculation unit may include a first expected hydrogen consumption calculation unit that calculates a first expected hydrogen consumption at a first point in time based on first real-time operation information at the first point in time, a second expected hydrogen consumption calculation unit that calculates a second expected hydrogen consumption at a second point in time later than the first point in time based on at least one of the real-time operation information and the driving pattern information, and a final expected hydrogen consumption calculation unit that calculates the final expected hydrogen consumption based on at least one of the first expected hydrogen consumption and the second expected hydrogen consumption.

[0011] According to one embodiment, the second expected hydrogen consumption calculation unit can use an artificial intelligence model that has learned the driver's existing driving pattern information to predict the driver's driving state at the second point in time based on the first real-time driving information at the first point in time, analyze the difference between the first point in time and the second point in time, and calculate the second expected hydrogen consumption corresponding to the change in expected hydrogen consumption.

[0012] According to one embodiment, the expected hydrogen consumption calculation unit predicts a maximum rapid acceleration state based on the real-time operation information and the driving pattern information, and calculates the expected hydrogen consumption required to stably supply hydrogen during the maximum rapid acceleration state.

[0013] According to one embodiment, the expected hydrogen consumption calculation unit can calculate the expected hydrogen consumption based on the real-time operation information, the driving pattern information, battery information of the device, and environmental information of the ammonia tank.

[0014] According to one embodiment, the real-time driving information includes at least one of a magnitude of pressure applied to an accelerator pedal, a change in a magnitude of pressure applied to the accelerator pedal, a magnitude of pressure applied to a brake pedal, a change in a magnitude of pressure applied to the brake pedal, a number of sudden accelerations during a preset period, a number of sudden brakings during the preset period, a driving speed, a driving distance, a driving time, and a driving mode, and the driving pattern information includes at least one of an acceleration pattern, a deceleration pattern, a usage ratio of a driving mode, an average driving speed, an average number of lane changes, and a vehicle internal environment.

[0015] An operating method of an apparatus for controlling a vehicle equipped with an ammonia fuel cell according to an embodiment of the present disclosure includes a step of calculating a predicted hydrogen consumption amount based on at least one of real-time operation information and driving pattern information, and a step of controlling at least one of the amount of ammonia supplied from the ammonia tank and the internal conditions of a cracker that decomposes the ammonia supplied from the ammonia tank into hydrogen based on the predicted hydrogen consumption amount. Effect of the Invention

[0016] According to an embodiment of the present disclosure, by monitoring minute changes in driving patterns based on real-time driving information, information on the driver's existing driving patterns, and the like, and controlling the ammonia fuel cell system, it is possible to stabilize the ammonia decomposition rate of the cracker and minimize residual ammonia.

[0017] In addition, according to one embodiment of the present disclosure, the amount of hydrogen production customized for the driver is controlled based on the driver's normal driving pattern, and information can be predicted even after a specific time has passed, which has the effect of predicting the required hydrogen consumption more accurately and preventing energy shortages at the moment when maximum output is required.

[0018] Moreover, according to one embodiment of the present disclosure, by optimizing the amount of ammonia supplied or the internal conditions of the cracker, there are effects of preventing the battery from being overcharged due to the supply of more ammonia than necessary, preventing the generation of residual ammonia, and stabilizing the ammonia decomposition rate. [Brief description of the drawings]

[0019] [Figure 1] FIG. 1 is a diagram for explaining an apparatus according to an embodiment of the present disclosure. [Diagram 2] FIG. 2 is a diagram for explaining various pieces of information contained in an apparatus according to an embodiment of the present disclosure. [Diagram 3]FIG. 3 is a diagram for explaining a controller according to an embodiment of the present disclosure. [Figure 4] FIG. 4 is a diagram for explaining the expected hydrogen consumption amount calculation unit according to an embodiment of the present disclosure. [Diagram 5] FIG. 5 is a diagram illustrating a method of operation of an apparatus according to an embodiment of the present disclosure. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0020] Hereinafter, embodiments of the present disclosure will be described in more detail with reference to the drawings. However, these embodiments are merely illustrative of the present disclosure and are not intended to limit the present disclosure.

[0021] The same reference numerals refer to the same components throughout this disclosure. This disclosure does not describe all elements of the embodiments, and general content in the technical field to which the disclosure belongs or content that is duplicated in the embodiments will be omitted. The term "part, module, member, block" used in the specification can be embodied in software or hardware, and depending on the embodiment, multiple "parts, modules, members, blocks" may be embodied as one component, or one "part, module, member, block" may include multiple components.

[0022] Throughout the specification, when a part is referred to as being "coupled" to another part, this includes not only direct coupling but also indirect coupling, including coupling via a wireless communication network.

[0023] Furthermore, unless otherwise specified, when a part "comprises" a certain component, it does not exclude other components but means that the part may further include other components.

[0024] Throughout the specification, a member is referred to as being "on" another member not only when the member is in contact with the other member, but also when there is another member between the two members.

[0025] The terms "first", "second", etc. are used only to distinguish one component from another component, and are not intended to limit the components.

[0026] The singular expression includes the plural expression unless the context clearly indicates otherwise.

[0027] The identification numbers in each step are used for ease of description and do not dictate the order of each step, and each step may be performed in a manner different from the stated order unless the context clearly dictates a particular order.

[0028] In this specification, the term "device according to an embodiment of the present disclosure" includes all of various devices capable of performing computation and providing a result to a user. For example, the device according to an embodiment of the present disclosure may include all of a computer, a server device, and a portable terminal, and may take any of these forms.

[0029] Here, the computer may include, for example, a notebook computer, a desktop computer, a laptop computer, a tablet PC, a slate PC, etc. equipped with a web browser.

[0030] The server device is a server that communicates with external devices and processes information, and may include an application server, a computing server, a database server, a file server, a game server, a mail server, a proxy server, a web server, etc.

[0031] The portable terminal may be, for example, a wireless communication device that ensures portability and mobility, and may include any kind of handheld-based wireless communication device such as a Personal Communication System (PCS), Global System for Mobile communications (GSM), Personal Digital Cellular (PDC), Personal Handyphone System (PHS), Personal Digital Assistant (PDA), International Mobile Telecommunication (IMT)-2000, Code Division Multiple Access (CDMA)-2000, W-Code Division Multiple Access (W-CDMA), Wireless Broadband Internet (WiBro) terminal, or a smartphone, as well as a wearable device such as a watch, a ring, a bracelet, an anklet, a necklace, glasses, contact lenses, or a head-mounted device (HMD).

[0032] The functions related to the artificial intelligence according to the present disclosure operate via a processor and a memory. The processor may be configured with one or more processors. In this case, the one or more processors may be general-purpose processors such as CPU, AP, DSP (Digital Signal Processor), graphics dedicated processors such as GPU, VPU (Vision Processing Unit), or artificial intelligence dedicated processors such as NPU. The one or more processors control the processing of input data according to predefined operating rules or artificial intelligence models stored in the memory. In addition, when the one or more processors are dedicated artificial intelligence processors, the dedicated artificial intelligence processors may be designed with a hardware structure specialized for processing a specific artificial intelligence model.

[0033] The predefined operation rules or artificial intelligence models are characterized by being created through learning. Here, "created through learning" means that a predefined operation rule or artificial intelligence model is created that is set to perform a desired characteristic (or purpose) by learning a basic artificial intelligence model using a large amount of learning data through a learning algorithm. Such learning may be performed in the device itself on which the artificial intelligence according to the present disclosure is executed, or may be performed via a separate server and / or system. Examples of the learning algorithm include supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning, but are not limited to the above examples.

[0034] The artificial intelligence model may be composed of a plurality of neural network layers. Each of the plurality of neural network layers has a plurality of weight values, and performs a neural network operation through an operation result of a previous layer and an operation between the plurality of weights. The plurality of weights of the plurality of neural network layers may be optimized according to a learning result of the artificial intelligence model. For example, the plurality of weights may be updated so that a loss value or a cost value acquired from the artificial intelligence model during the learning process is reduced or minimized. The artificial neural network may include a deep neural network (DNN), such as a convolutional neural network (CNN), a deep neural network (DNN), a recurrent neural network (RNN), a restricted Boltzmann machine (RBM), a deep belief network (DBN), a bidirectional recurrent deep neural network (BRDNN), or a deep Q-network, but is not limited to the above examples.

[0035] According to an exemplary embodiment of the present disclosure, the processor may embody artificial intelligence. Artificial intelligence refers to a machine learning method based on an artificial neural network that imitates human biological neurons and allows a machine to learn. The methodology of artificial intelligence can be classified into supervised learning, in which input data and output data are provided together as training data according to a learning method, so that an answer (output data) to a problem (input data) is determined, unsupervised learning, in which only input data is provided without output data, so that an answer (output data) to a problem (input data) is not determined, and reinforcement learning, in which a reward is given in an external environment every time a certain action is taken in a current state, and learning is performed in a direction to maximize the reward. Artificial intelligence methodologies can also be categorized by architecture, which is the structure of the learning model. The architectures of widely used deep learning technologies can be categorized into convolutional neural networks (CNNs), recurrent neural networks (RNNs), Transformers, and generative adversarial networks (GANs), etc.

[0036] The device and system may include an artificial intelligence model. The artificial intelligence model may be one artificial intelligence model, or may be embodied as a plurality of artificial intelligence models. The artificial intelligence model may be configured as a neural network (or an artificial neural network) and may include a statistical learning algorithm that mimics biological nerves in machine learning and cognitive science. A neural network may refer to a general model in which artificial neurons (nodes) forming a network by synaptic connections change the strength of synaptic connections through learning and have a problem-solving ability. Neurons in a neural network may include a combination of weights or biases. A neural network may include one or more layers composed of one or more neurons or nodes. For example, the device may include an input layer, a hidden layer, and an output layer. The neural network constituting the device may infer a result to be predicted from an arbitrary input by changing the weights of neurons through learning.

[0037] The processor may generate a neural network, train or learn the neural network, perform an operation based on received input data and generate an information signal based on the operation result, or retrain the neural network. The neural network model may include various types of models such as, but not limited to, Convolution Neural Networks (CNNs) such as GoogleNet, AlexNet, and VGG Network, Region with Convolution Neural Networks (R-CNNs), Region Proposal Networks (RPNs), Recurrent Neural Networks (RNNs), Stacking-based deep Neural Networks (S-DNNs), State-Space Dynamic Neural Networks (S-SDNNs), Deconvolution Networks, Deep Belief Networks (DBNs), Restricted Boltzman Machines (RBMs), Fully Convolutional Networks, Long Short-Term Memory Networks (LSTMs), and Classification Networks. The processor may include one or more processors for performing an operation according to the neural network model. For example, the neural network may include a deep neural network.

[0038] Neural networks include CNN (Convolutional Neural Network), RNN (Recurrent Neural Network), perceptron, multilayer perceptron, FF (Feed Forward), RBF (Radial Basis Network), DFF (Deep Feed Forward), LSTM (Long Short Term Memory), GRU (Gated Recurrent Unit), AE (Auto Encoder), VAE (Variational Auto Encoder), DAE (Denoising Auto Encoder), SAE (Sparse Auto Encoder), MC (Markov Chain), HN (Hopfield Network), BM (Boltzmann Machine), RBM (Restricted Boltzmann Machine), DBN (Deep Belief Network), DCN (Deep Convolutional Network), DN (Deconvolutional Network), DCIGN (Deep Convolutional Inverse Graphics Network), GAN (Generative Adversarial Network), LSM (Liquid As will be understood by those of ordinary skill in the art, the neural network may include any neural network, including, but not limited to, an ELM (Extreme Learning Machine), an ESN (Echo State Network), a DRN (Deep Residual Network), a DNC (Differentiable Neural Computer), an NTM (Neural Turning Machine), a CN (Capsule Network), a KN (Kohonen Network), and an AN (Attention Network).

[0039] According to an exemplary embodiment of the present disclosure, the processor may implement a number of neural networks, including Convolution Neural Networks (CNNs) such as GoogleNet, AlexNet, and VGG Network, Region with Convolution Neural Network (R-CNN), Region Proposal Network (RPN), Recurrent Neural Network (RNN), Stacking-based deep Neural Network (S-DNN), State-Space Dynamic Neural Network (S-SDNN), Deconvolution Network, Deep Belief Network (DBN), Restricted Boltzman Machine (RBM), Fully Convolutional Network, Long Short-Term Memory (LSTM) Network, Classification Network, Generative Modeling, eXplainable AI, Continual AI, Representation Learning, AI for Material Design, BERT, SP-BERT, MRC / QA, Text Analysis, Dialog System, GPT-3, GPT-4 for Natural Language Processing, Visual Analytics, Visual Understanding, Video Synthesis, ResNet for Data Intelligence, Anomaly Detection, Prediction, Time-Series Various artificial intelligence structures and algorithms may be used, including but not limited to, forecasting, optimization, recommendation, data creation, etc. Hereinafter, embodiments of the present disclosure will be described in detail with reference to the accompanying drawings.

[0040] FIG. 1 is a diagram for explaining an apparatus according to an embodiment of the present disclosure.

[0041] Referring to Fig. 1, the apparatus 100 can control a vehicle equipped with an ammonia fuel cell. The apparatus 100 can predict hydrogen consumption required for vehicle operation from a microscopic perspective based on real-time operation information and / or driving pattern information. The apparatus 100 can then control the vehicle equipped with an ammonia fuel cell based on the predicted hydrogen consumption. The apparatus 100 can include an ammonia tank 110, a cracker 120, a controller 130, and an interface 140.

[0042] The ammonia tank 110 can store ammonia. The ammonia tank 110 can provide ammonia to the cracker 120 at a supply amount of ammonia determined by the control of the controller 130.

[0043] The cracker 120 can decompose the ammonia supplied from the ammonia tank 110 into hydrogen. The cracker 120 can decompose the ammonia into hydrogen under the internal conditions of the cracker 120 determined by the control of the controller 130.

[0044] The controller 130 may calculate the expected hydrogen consumption amount based on at least one of the real-time operation information and the driving pattern information, and may control at least one of the ammonia supply amount and the internal conditions of the cracker based on the expected hydrogen consumption amount.

[0045] The real-time driving information may refer to vehicle driving information of a user (or a driver) that is monitored in real time from a microscopic perspective.

[0046] The driving pattern information may refer to information about a driver's normal driving patterns (e.g., driving habits) that the artificial intelligence model has learned.

[0047] The control method of the controller 130 may include a method for controlling the amount of ammonia supplied to the cracker 120 and / or a method for controlling the internal conditions of the cracker 120.

[0048] The controller 130 can correct the amount of hydrogen consumption required to maintain the current operating state, taking into account the amount of hydrogen consumption required for the operating state predicted at a time point after the current time point.

[0049] The controller 130 may include a memory (not shown) that stores data related to an algorithm or a program that reproduces the algorithm for controlling the operation of components in the device 100, and at least one processor that performs a control operation using the data stored in the memory. In this case, the memory and the processor may be implemented on separate chips, or the memory and the processor may be implemented on a single chip.

[0050] The interface 140 can provide an interfacing function that allows selection of an automatic control mode for automatically controlling the amount of ammonia supplied, or a manual control mode for manually controlling the amount of ammonia supplied.

[0051] Although not shown, device 100 may further include an input unit for inputting video information (or signals), audio information (or signals), data, or information input by a user, a communication unit including one or more components enabling communication with an external device, an audio output unit for outputting audio data received from the outside or stored in memory, or for outputting audio signals associated with functions performed by device 100, a display unit for displaying information processed by device 100, and a battery.

[0052] The memory can store data supporting various functions of the device 100 and programs for the operation of the controller 130, can store input / output data (e.g., music files, still images, videos, etc.), can store a number of application programs (or applications) run by the device 100, and can store data and commands for the operation of the device 100. At least some of these application programs can be downloaded from an external server via wireless communication. Such memory may include at least one of the following types of storage media: flash memory type, hard disk type, solid state disk type, silicon disk drive type, multimedia card micro type, card type memory (such as SD or XD memory), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, magnetic disk, and optical disk. The memory may also be a database separate from the device 100 but connected by wire or wirelessly.

[0053] As mentioned above, by monitoring minute changes in driving patterns based on real-time driving information and information on the driver's existing driving patterns, the ammonia fuel cell system is controlled to stabilize the ammonia decomposition rate of the cracker and minimize residual ammonia.

[0054] In addition, as mentioned above, by controlling the amount of hydrogen production customized for the driver based on the driver's normal driving pattern and predicting information at a point in time after a specific period of time has passed, it is possible to more accurately predict the amount of hydrogen consumption required and prevent energy shortages at the moment when maximum output is required.

[0055] In addition, as described above, by optimizing the amount of ammonia supplied or the internal conditions of the cracker, it is possible to prevent the battery from being overcharged due to the supply of more ammonia than necessary, to prevent the generation of residual ammonia, and to stabilize the ammonia decomposition rate.

[0056] FIG. 2 is a diagram for explaining various pieces of information contained in an apparatus according to an embodiment of the present disclosure.

[0057] 2, various information 200 included in the device can be stored in a memory. The various information 200 can include real-time operation information 210, driving pattern information 220, first estimated hydrogen consumption amount information 230, second estimated hydrogen consumption amount information 240, optimal ammonia supply amount information 250, and optimal cracker internal condition information 260.

[0058] The real-time driving information 210 may include, for example, one or more of the magnitude of pressure applied to the accelerator pedal, the amount of change in the magnitude of pressure applied to the accelerator pedal, the magnitude of pressure applied to the brake pedal, the amount of change in the magnitude of pressure applied to the brake pedal, the number of sudden accelerations in a preset period, the number of sudden brakings in a preset period, driving speed, mileage, driving time, and driving mode.

[0059] The driving pattern information 220 may include, for example, one or more of an acceleration pattern, a deceleration pattern, a usage ratio of a drive mode, an average driving speed, an average number of lane changes, and a vehicle interior environment.

[0060] The drive modes may include, for example, one or more of Comfort, Eco, Sport, Terrain, Off-road, etc.

[0061] The first expected hydrogen consumption information 230 and the second expected hydrogen consumption information 240 can be generated using the real-time operation information 210 and the driving pattern information 220, respectively.

[0062] The first expected hydrogen consumption information 230 may include a first expected hydrogen consumption amount at a first time point. In some embodiments, the first time point may be the present time point.

[0063] The second expected hydrogen consumption information 240 may be information including a second expected hydrogen consumption at a second time point. The second time point may be a time point a certain amount of time has elapsed after the first time point. In some embodiments, the first time point may be the current time point, and the second time point may be any time point after the current time point. However, the present invention is not limited to this.

[0064] The appropriate ammonia supply amount information 250 and / or the appropriate cracker internal condition information 260 may be generated using the first expected hydrogen consumption amount information 230 and the second expected hydrogen consumption amount information 240. The appropriate ammonia supply amount information 250 and / or the appropriate cracker internal condition information 260 may be generated by the controller 130.

[0065] The appropriate ammonia supply amount information 250 may include information indicating the amount of ammonia that the ammonia tank 110 supplies to the cracker 120 .

[0066] The appropriate cracker internal condition information 260 may include information indicative of the internal conditions of the cracker 120. The internal conditions of the cracker 120 may include, for example, one or more of an internal temperature, an internal pressure, an internal humidity, an ammonia flow rate, and a hydrogen flow rate.

[0067] Although not shown, the various information 200 may further include battery information of the device 100 and environmental information of the ammonia tank 110. The battery information may be information indicating the state of a battery provided in the device 100. The environmental information of the ammonia tank 110 may be information including, for example, one or more of the temperature, pressure, humidity, and the like of the ammonia tank.

[0068] FIG. 3 is a diagram for explaining a controller according to an embodiment of the present disclosure.

[0069] 3, a controller 300 according to some embodiments may control the amount of ammonia supplied based on the expected amount of hydrogen consumption. The controller 300 according to some embodiments may include an expected amount of hydrogen consumption calculation unit 310 and an ammonia supply amount control unit 320.

[0070] The expected hydrogen consumption calculation unit 310 can calculate a plurality of expected hydrogen consumptions at a specific time point based on at least one of the real-time operation information for each specific time point 210 and the driving pattern information 220. The expected hydrogen consumption calculation unit 310 can then calculate a final expected hydrogen consumption based on at least one of the plurality of expected hydrogen consumptions.

[0071] In some embodiments, the expected hydrogen consumption calculation unit 310 can predict the maximum rapid acceleration state based on real-time operation information and driving pattern information, and can calculate the expected hydrogen consumption amount required to stably supply hydrogen during the maximum rapid acceleration state.

[0072] In some other embodiments, the expected hydrogen consumption calculation unit 310 can calculate the expected hydrogen consumption based on real-time operation information, driving pattern information, battery information of the device, and environmental information of the ammonia tank.

[0073] The ammonia supply amount control unit 320 can calculate the amount of ammonia supply required to secure the final expected hydrogen consumption amount. The ammonia supply amount control unit 320 can perform a control operation of the piping and valve system so that ammonia is supplied to the cracker by the ammonia supply amount. The control operation of the piping and valve system can mean various control operations related to the piping and valve system.

[0074] The controller 300 according to some other embodiments may control the internal conditions of the cracker 120 based on the predicted hydrogen consumption. The controller 300 according to some other embodiments may include a predicted hydrogen consumption calculation unit 310 and a cracker condition control unit 330.

[0075] The cracker condition control unit 330 can control the internal conditions of the cracker 120 based on the amount of ammonia decomposed into hydrogen in the cracker 120 and the final expected hydrogen consumption. For example, the cracker condition control unit 330 can determine the temperature, pressure, humidity, flow rate, etc. inside the cracker 120 so that the amount of decomposition does not exceed or fall short of the final expected hydrogen consumption calculated by the expected hydrogen consumption calculation unit 310 (or so that the amount of ammonia decomposed into hydrogen in the cracker 120 reaches the final expected hydrogen consumption).

[0076] In addition, the controller 300 according to some other embodiments may control the amount of ammonia supplied and the internal conditions of the cracker 120 based on the expected hydrogen consumption. In addition, the controller 300 according to some other embodiments may include an expected hydrogen consumption calculation unit 310, an ammonia supply amount control unit 320, and a cracker condition control unit 330.

[0077] FIG. 4 is a diagram for explaining the expected hydrogen consumption amount calculation unit according to an embodiment of the present disclosure.

[0078] Referring to FIG. 4, the expected hydrogen consumption calculation unit 400 may include a first expected hydrogen consumption calculation unit 410, a second expected hydrogen consumption calculation unit 420, and a final expected hydrogen consumption calculation unit 430.

[0079] The first expected hydrogen consumption calculation unit 410 can calculate a first expected hydrogen consumption at a first time point based on the first real-time operation information at a first time point. The first expected hydrogen consumption calculation unit 410 can calculate a hydrogen consumption amount for maintaining an operation state at a first time point (e.g., a current time point). For example, the first expected hydrogen consumption calculation unit 410 can calculate the first expected hydrogen consumption at the first time point based on the running speed, acceleration rate of change or deceleration rate of change, drive mode, operation status of an air conditioning device (e.g., air conditioner, heater, etc.) at the first time point, etc.

[0080] The second expected hydrogen consumption calculation unit 420 can calculate a second expected hydrogen consumption at a second time point later than the first time point based on at least one of the real-time operation information 210 and the driving pattern information 220. The second expected hydrogen consumption calculation unit 420 can calculate a hydrogen consumption for changing the driving state predicted at the second time point (e.g., a time point when a specific time has passed from the current time point). For example, the second expected hydrogen consumption calculation unit 420 can predict the driving state at the second time point using an artificial intelligence model that has learned the existing driving pattern information of the driver. As a specific example, the second expected hydrogen consumption calculation unit 420 can predict the driving state of the driver at the second time point based on the first real-time operation information at the first time point using an artificial intelligence model that has learned the existing driving pattern information of the driver. Then, the second expected hydrogen consumption calculation unit 420 can analyze the difference between the first time point and the second time point and calculate a second expected hydrogen consumption corresponding to the change in the expected hydrogen consumption.

[0081] The final expected hydrogen consumption calculation unit 430 may calculate the final expected hydrogen consumption based on at least one of the first expected hydrogen consumption and the second expected hydrogen consumption. In some embodiments, the final expected hydrogen consumption calculation unit 430 may calculate the final expected hydrogen consumption using the first expected hydrogen consumption and the second expected hydrogen consumption.

[0082] FIG. 5 is a diagram illustrating a method of operation of an apparatus according to an embodiment of the present disclosure.

[0083] 1 and 5, the method of operation of the apparatus 100 may be a method of controlling a vehicle equipped with an ammonia fuel cell.

[0084] In step S100, the device 100 can calculate an expected hydrogen consumption amount based on at least one of real-time operation information and driving pattern information. The embodiment related to step S100 can be performed by the expected hydrogen consumption amount calculation unit 310 in FIG. 3 and the expected hydrogen consumption amount calculation unit 400 in FIG. 4.

[0085] In step S200, the apparatus 100 can control at least one of the amount of ammonia supplied from the ammonia tank 110 based on the expected hydrogen consumption amount and the internal conditions of the cracker 120 that decomposes the ammonia supplied from the ammonia tank 110 into hydrogen. The embodiment related to step S200 can be performed by the controller 130 of FIG. 1, the ammonia supply amount control unit 320 of FIG. 3, and / or the cracker condition control unit 330 of FIG. 3.

[0086] Meanwhile, the disclosed embodiments may be embodied in the form of a storage medium storing instructions executable by a computer. The instructions may be stored in the form of program code, and when executed by a processor, may generate program modules to perform the operations of the disclosed embodiments. The storage medium may be embodied as a computer-readable storage medium.

[0087] The computer-readable storage medium includes any kind of storage medium in which instructions that can be decoded by a computer are stored, such as a Read Only Memory (ROM), a Random Access Memory (RAM), a magnetic tape, a magnetic disk, a flash memory, an optical data storage device, and the like.

[0088] As described above, the disclosed embodiments have been described with reference to the accompanying drawings. Those skilled in the art will understand that the present disclosure may be implemented in a form different from the disclosed embodiments without changing the technical idea or essential features of the present disclosure. The disclosed embodiments are illustrative and should not be construed as being limited.

Claims

1. In a device for controlling a vehicle equipped with an ammonia fuel cell, an ammonia tank for storing ammonia; a cracker that decomposes the ammonia supplied from the ammonia tank into hydrogen; and a controller that calculates a predicted hydrogen consumption amount based on at least one of real-time operation information and driving pattern information, and controls at least one of the ammonia supply amount and the internal conditions of the cracker based on the predicted hydrogen consumption amount.

2. The controller includes: an expected hydrogen consumption calculation unit that calculates a plurality of expected hydrogen consumption amounts at a specific time point based on at least one of the real-time operation information and the driving pattern information for each specific time point, and calculates a final expected hydrogen consumption amount based on at least one of the plurality of expected hydrogen consumption amounts; 2. The apparatus according to claim 1, further comprising: an ammonia supply amount control unit that calculates the ammonia supply amount necessary to secure the final predicted hydrogen consumption amount, and controls a piping and valve system so that the ammonia can be supplied to the cracker by the calculated supply amount of the ammonia.

3. The controller includes: an expected hydrogen consumption calculation unit that calculates a plurality of expected hydrogen consumption amounts at a specific time point based on at least one of the real-time operation information and the driving pattern information for each specific time point, and calculates a final expected hydrogen consumption amount based on at least one of the plurality of expected hydrogen consumption amounts; 2. The apparatus according to claim 1, further comprising a cracker condition control unit for controlling an internal condition of the cracker based on an amount of ammonia decomposed into the hydrogen in the cracker and the final predicted hydrogen consumption.

4. The controller includes: an expected hydrogen consumption calculation unit that calculates a plurality of expected hydrogen consumption amounts at a specific time point based on at least one of the real-time operation information and the driving pattern information for each specific time point, and calculates a final expected hydrogen consumption amount based on at least one of the plurality of expected hydrogen consumption amounts; an ammonia supply amount control unit that calculates the amount of ammonia supply necessary to secure the final expected hydrogen consumption amount, and controls a piping and a valve system so that the ammonia can be supplied to the cracker by the calculated amount of ammonia supply; and a cracker condition control unit for setting internal conditions of the cracker so that an amount of the ammonia decomposed into the hydrogen in the cracker reaches the final expected hydrogen consumption amount.

5. The predicted hydrogen consumption calculation unit is a first expected hydrogen consumption calculation unit that calculates a first expected hydrogen consumption at a first time point based on first real-time operation information at the first time point; a second expected hydrogen consumption calculation unit that calculates a second expected hydrogen consumption at a second time point that is later than the first time point based on at least one of the real-time operation information and the driving pattern information; and a final expected hydrogen consumption calculation unit that calculates the final expected hydrogen consumption amount based on at least one of the first expected hydrogen consumption amount and the second expected hydrogen consumption amount.

6. The second expected hydrogen consumption calculation unit is predicting a driving state of the driver at the second time point based on the first real-time driving information at the first time point using an artificial intelligence model that has learned existing driving pattern information of the driver; 6. The apparatus of claim 5, further comprising: analyzing a difference between the first time point and the second time point to calculate the second expected hydrogen consumption rate corresponding to a change in expected hydrogen consumption rate.

7. The predicted hydrogen consumption calculation unit is Predicting a maximum rapid acceleration state based on the real-time operation information and the driving pattern information; The apparatus of claim 4 , further comprising: a calculating section for calculating the predicted hydrogen consumption required to provide a stable supply of hydrogen under the maximum rapid acceleration condition.

8. The predicted hydrogen consumption calculation unit is The device of claim 4 , further comprising: a device for calculating the predicted hydrogen consumption amount based on the real-time operation information, the driving pattern information, the battery information of the device, and the environmental information of the ammonia tank.

9. The real-time operation information is at least one of a pressure applied to an accelerator pedal, a change in the pressure applied to the accelerator pedal, a pressure applied to a brake pedal, a change in the pressure applied to the brake pedal, a number of sudden accelerations in a preset period, a number of sudden brakings in the preset period, a driving speed, a driving distance, a driving time, and a driving mode; The driving pattern information is The apparatus of claim 1 , comprising at least one of an acceleration pattern, a deceleration pattern, a usage ratio of a drive mode, an average driving speed, an average number of lane changes, and a vehicle interior environment.

10. A method for operating an apparatus for controlling a vehicle equipped with an ammonia fuel cell, comprising: Calculating an expected hydrogen consumption amount based on at least one of real-time operation information and driving pattern information; and controlling at least one of an amount of ammonia supplied from an ammonia tank based on the predicted hydrogen consumption amount and an internal condition of a cracker that cracks the ammonia supplied from the ammonia tank into hydrogen.

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