Apparatus for controlling vehicle equipped with ammonia fuel cell and method for operating same

Abstract

The present disclosure provides an apparatus for controlling a vehicle carrying an ammonia fuel cell and an operating method thereof. The apparatus of the present disclosure may include: an ammonia tank storing ammonia; a decomposer 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 the real-time operation information and the driving mode information, and controls at least one of the ammonia supply amount and an internal condition of the resolver based on the predicted hydrogen consumption amount.

Description

Technical Field

[0001] The present disclosure relates to a device for controlling a vehicle equipped with an ammonia fuel cell and an operating method thereof. Background Art

[0002] The vehicle comprises a drive device which can move the vehicle by receiving energy from an energy source. An internal combustion engine running on fuel is one of the most widely used energy sources. However, the combustion of fuel produces carbon dioxide which pollutes the atmosphere, so it is preferred to use an energy source that is less polluting.

[0003] It is well known that fuel cells, such as hydrogen cells, are used to replace internal combustion engines. When hydrogen in the form of dihydrogen is oxidized by the fuel cell, the battery will generate electricity for supplying energy to the drive device and generate heat. Hydrogen can be stored in the form of gaseous ammonia absorbed or adsorbed by the salt in the storage box (cartridge). This is a reliable method for storing hydrogen. Therefore, in order to form dihydrogen that can be supplied to the fuel cell, ammonia must be desorbed from the salt and then cracked. As long as this storage is carried out by absorption or adsorption, the terms "absorption" and "desorption" will be used to refer to the storage of gaseous ammonia in the salt and the release of gaseous ammonia from the salt, respectively.

[0004] When a vehicle equipped with an ammonia fuel cell is running, it is necessary to stabilize the ammonia decomposition rate of the decomposer and minimize residual ammonia. To this end, research is being conducted to control the ammonia fuel cell system by monitoring subtle driving pattern changes based on real-time operating information, the driver's current driving pattern information, etc. Summary of the invention

[0005] 1. Technical issues to be resolved

[0006] According to one aspect of the present disclosure, a device for controlling a vehicle equipped with an ammonia fuel cell and an operating method thereof can be provided. The device monitors subtle driving mode changes based on real-time operating information, the driver's existing driving mode information, etc. and controls the ammonia fuel cell system, thereby stabilizing the ammonia decomposition rate of the decomposer, contributing to environmental protection, and generating clean energy.

[0007] (II) Technical solution

[0008] According to one embodiment of the present disclosure, a device for controlling a vehicle equipped with an ammonia fuel cell may include: an ammonia tank for storing ammonia; a decomposer for decomposing ammonia supplied from the ammonia tank into hydrogen; and a controller for calculating a predicted hydrogen consumption based on at least one of real-time operating information and driving mode information, and controlling at least one of the ammonia supply and an internal condition of the decomposer based on the predicted hydrogen consumption.

[0009] According to one embodiment, the controller may include: a predicted hydrogen consumption calculation unit, which calculates multiple predicted hydrogen consumptions at a specific time point based on the real-time operation information and at least one of the driving mode information at each specific time point, and calculates a final predicted hydrogen consumption based on at least one of the multiple predicted hydrogen consumptions; and an ammonia supply control unit, which calculates the ammonia supply required to ensure the final predicted hydrogen consumption, and performs pipeline and valve system control operations to supply ammonia equivalent to the ammonia supply to the decomposer.

[0010] According to one embodiment, the controller may include: a predicted hydrogen consumption calculation unit, which calculates multiple predicted hydrogen consumptions at a specific time point based on the real-time operation information and at least one of the driving mode information at each specific time point, and calculates a final predicted hydrogen consumption based on at least one of the multiple predicted hydrogen consumptions; and a decomposer condition control unit, which controls the internal conditions of the decomposer based on the decomposition amount of the ammonia into the hydrogen in the decomposer and the final predicted hydrogen consumption.

[0011] According to one embodiment, the controller may include: a predicted hydrogen consumption calculation unit, which calculates multiple predicted hydrogen consumptions at a specific time point based on the real-time operation information and at least one of the driving mode information at each specific time point, and calculates a final predicted hydrogen consumption based on at least one of the multiple predicted hydrogen consumptions; an ammonia supply control unit, which calculates the ammonia supply required to ensure the final predicted hydrogen consumption, and performs pipeline and valve system control operations to supply ammonia equivalent to the ammonia supply to the decomposer; and a decomposer condition control unit, which sets the internal conditions of the decomposer so that the decomposition amount of the ammonia into the hydrogen in the decomposer reaches the final predicted hydrogen consumption.

[0012] According to one embodiment, the predicted hydrogen consumption calculation unit may include: a first predicted hydrogen consumption calculation unit, which calculates a first predicted hydrogen consumption at a first time point based on first real-time operation information at the first time point; a second predicted hydrogen consumption calculation unit, which calculates a second predicted hydrogen consumption at a second time point later than the first time point based on at least one of the real-time operation information and the driving mode information; and a final predicted hydrogen consumption calculation unit, which calculates the final predicted hydrogen consumption based on at least one of the first predicted hydrogen consumption and the second predicted hydrogen consumption.

[0013] According to one embodiment, the second predicted hydrogen consumption calculation unit can utilize an artificial intelligence model that learns the driver's existing driving pattern information to predict the driver's operating status at the second time point based on the first real-time operating information at the first time point, and can analyze the difference between the first time point and the second time point, and calculate the second predicted hydrogen consumption corresponding to the change in predicted hydrogen consumption.

[0014] According to one embodiment, the predicted hydrogen consumption calculation unit may predict a maximum rapid acceleration state based on the real-time operation information and the driving mode information, and may calculate the predicted hydrogen consumption required for stably supplying the hydrogen in the maximum rapid acceleration state.

[0015] According to one embodiment, the predicted hydrogen consumption calculation unit may calculate the predicted hydrogen consumption based on the real-time operation information, the driving mode information, the battery information of the device, and the environmental information of the ammonia tank.

[0016] According to one embodiment, the real-time operating information may include one or more of the amount of pressure applied to the accelerator pedal, the amount of change in the amount of pressure applied to the accelerator pedal, the amount of pressure applied to the brake pedal, the amount of change in the amount of pressure applied to the brake pedal, the number of sudden accelerations within a preset period, the number of sudden braking within the preset period, the driving speed, the driving distance, the driving time and the driving mode, and the driving mode information may include one or more of the acceleration mode, the deceleration mode, the usage ratio of the driving mode, the average driving speed, the average number of lane changes and the vehicle's internal environment.

[0017] According to one embodiment, an operating method of an apparatus for controlling a vehicle equipped with an ammonia fuel cell may include: a step of calculating a predicted hydrogen consumption based on at least one of real-time operating information and driving mode information; and a step of controlling at least one of an amount of ammonia supplied from an ammonia tank and an internal condition of a decomposer that decomposes the ammonia supplied from the ammonia tank into hydrogen based on the predicted hydrogen consumption.

[0018] (III) Beneficial effects

[0019] According to one embodiment of the present disclosure, subtle driving pattern changes are monitored and the ammonia fuel cell system is controlled based on real-time operation information, the driver's existing driving pattern information, etc., thereby having the effect of stabilizing the ammonia decomposition rate of the decomposer and minimizing residual ammonia.

[0020] In addition, according to one embodiment of the present disclosure, the amount of hydrogen produced is customized for the driver based on the driver's usual driving pattern control and information at a time point after a specific time has passed is predicted, thereby having the effect of more accurately predicting the required hydrogen consumption and preventing energy shortage when maximum output is required.

[0021] In addition, according to one embodiment of the present disclosure, the ammonia supply amount or the internal conditions of the decomposer are optimized, thereby having the effects of preventing the battery from being overcharged due to the ammonia supply amount exceeding the required amount, preventing the generation of residual ammonia, and stabilizing the ammonia decomposition rate. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 is a diagram for explaining a device according to an embodiment of the present disclosure.

[0023] Figure 2 is a diagram for explaining various information included in a device according to an embodiment of the present disclosure.

[0024] Figure 3 is a diagram for explaining a controller according to an embodiment of the present disclosure.

[0025] Figure 4 is a diagram for explaining a predicted hydrogen consumption calculation unit according to one embodiment of the present disclosure.

[0026] Figure 5 It is a diagram for explaining an operating method of a device according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0027] Hereinafter, the present disclosure will be described in detail with reference to the accompanying drawings. However, this is merely exemplary, and the present disclosure is not limited to the specific embodiments exemplarily described.

[0028] In the present disclosure, the same reference numerals refer to the same components. Not all elements of the embodiments are described in the present disclosure, and general contents of the technical field to which the present disclosure belongs or contents repeated between embodiments are omitted. The terms "unit, module, component, block" used in the specification can be implemented by software or hardware, and according to the embodiment, multiple "units, modules, components, blocks" can be implemented by a single component, or one "unit, module, component, block" can include multiple components.

[0029] Throughout the specification, when one part is referred to as being “connected” to another part, this includes not only the case of being directly connected but also the case of being indirectly connected including being connected through a wireless communication network.

[0030] In addition, when a section is referred to as “comprising” a component, unless specifically stated otherwise, it means that other components may be further included, rather than excluding other components.

[0031] Throughout the specification, when a member is located “on” another member, this includes not only a case where one member is in contact with another member but also a case where other members exist between the two members.

[0032] Terms such as first, second, etc. are used to distinguish one component from another component, and the components are not limited by the above terms.

[0033] Unless the context clearly indicates otherwise, an expression in the singular includes an expression in the plural.

[0034] The reference numerals of the steps are used for convenience of description and do not indicate the order of the steps. Unless the context clearly indicates a specific order, the steps may be performed in an order different from the specified order.

[0035] In this specification, "a device according to an embodiment of the present disclosure" includes all kinds of devices that can perform computing and provide results to users. For example, a device according to an embodiment of the present disclosure may include a computer, a server device, and a portable terminal, or any form thereof.

[0036] Here, the computer may include, for example, a notebook computer equipped with a web browser, a desktop computer, a laptop computer, a tablet PC, a touch-screen tablet PC, etc.

[0037] A server device is a server that processes information by communicating with external devices, 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 network server, and the like.

[0038] A portable terminal is, for example, a wireless communication device that ensures portability and mobility, and may include all types of handheld wireless communication devices such as PCS (Personal Communication System), GSM (Global System for Mobile communications), PDC (Personal Digital Cellular), PHS (Personal Handyphone System), PDA (Personal Digital Assistant), IMT (International Mobile Telecommunication)-2000, CDMA (Code Division Multiple Access)-2000, W-CDMA (W-Code Division Multiple Access), WiBro (Wireless Broadband Internet), terminals, smart phones, and wearable devices such as watches, rings, bracelets, anklets, necklaces, glasses, contact lenses, or head-mounted devices (HMD).

[0039] The functions related to artificial intelligence according to the present disclosure are operated by a processor and a memory. The processor may be composed of one or more processors. At this time, one or more processors may be general-purpose processors such as a CPU (central processing unit), an AP or a DSP (digital signal processor), a graphics-specific processor such as a GPU (graphics processing unit) or a VPU (vision processing unit), or an artificial intelligence-specific processor such as an NPU (neural network processor). One or more processors control to process input data according to predefined operating rules or artificial intelligence models stored in the memory. Alternatively, when one or more processors are artificial intelligence-specific processors, the artificial intelligence-specific processors can be designed as a hardware structure specifically for processing a specific artificial intelligence model.

[0040] The characteristics of the predefined operating rules or artificial intelligence models are established through learning. Here, established through learning means that the basic artificial intelligence model is learned by a learning algorithm using a large amount of learning data, thereby establishing predefined operating rules or artificial intelligence models to perform the desired characteristics (or purposes). This learning can be performed in the device itself that executes the artificial intelligence according to the present disclosure, or it can be performed by a separate server and / or system. Examples of learning algorithms include supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning, but are not limited to the above examples.

[0041] The artificial intelligence model can be composed of multiple neural network layers. Each of the multiple neural network layers has multiple weight values, and the neural network operation is performed by the operation result of the previous layer and the operation between the multiple weight values. The multiple weight values ​​of the multiple neural network layers can be optimized according to the learning results of the artificial intelligence model. For example, during the learning process, multiple weight values ​​can be updated to reduce or minimize the loss value or cost value obtained from the artificial intelligence model. The artificial neural network may include a deep neural network (Deep Neural Network, DNN), such as a convolutional neural network (Convolutional Neural Network, CNN), a deep neural network (Deep Neural Network, DNN), a recurrent neural network (Recurrent Neural Network, RNN), a restricted Boltzmann machine (Restricted Boltzmann Machine, RBM), a deep belief network (Deep Belief Network, DBN), a bidirectional recursive deep neural network (Bidirectional Recurrent Deep Neural Network, BRDNN) or a deep Q network (Deep Q-Networks), etc., but not limited to the above examples.

[0042] According to an exemplary embodiment of the present disclosure, the processor can implement artificial intelligence. Artificial intelligence refers to a machine learning method based on an artificial neural network (ANN), which enables the machine to learn by imitating human biological neurons (biological neuron). According to the learning method, the method of artificial intelligence can be divided into supervised learning, unsupervised learning and reinforcement learning, wherein the supervised learning is to provide input data and output data as learning data at the same time, so that the answer (output data) of the question (input data) has been determined, the unsupervised learning is to provide only input data without output data, so that the answer (output data) of the question (input data) has not been determined, and the reinforcement learning is that whenever an action (Action) is taken in the current state (State), the external environment will give a reward (Reward) so that the learning is carried out in the direction of maximizing the reward. In addition, the method of artificial intelligence can be distinguished according to the architecture of the structure as a learning model, and the architecture of the widely used deep learning technology can be distinguished as convolutional neural network (CNN), recursive neural network (RNN), Transformer, generative adversarial network (GAN), etc.

[0043] The present device and system may include an artificial intelligence model. The artificial intelligence model may be one artificial intelligence model or multiple artificial intelligence models. The artificial intelligence model may be composed of a neural network (or artificial neural network), and may include statistical learning algorithms that imitate biological neurons in machine learning and cognitive science. A neural network may refer to an overall model in which artificial neurons (nodes) that form a network through the combination of synapses change the binding strength of synapses through learning, and have problem-solving capabilities. Neurons in a neural network may include a combination of weight values ​​or biases. A neural network may include more than one layer consisting of one or more neurons or nodes. As an example, a device may include an input layer, a hidden layer, and an output layer. The neural network constituting the device can change the weight values ​​of neurons through learning, so that the result to be predicted (output) can be inferred from any input (input).

[0044] The processor may generate a neural network, train or learn a neural network, perform operations based on received input data, generate information signals based on execution results, or retrain a neural network. The neural network model may include various types of models such as CNN (convolutional neural network) such as GoogleNet, AlexNet, VGG network, R-CNN (Region with Convolution Neural Network), RPN (Region Proposal Network), RNN (Recurrent Neural Network), S-DNN (Stacking-based deep Neural Network), S-SDNN (State-Space Dynamic Neural Network), Deconvolution Network, DBN (Deep Belief Network), RBM (Restrcted Boltzman Machine), Fully Convolutional Network, LSTM (Long Short-Term Memory) network, Classification Network, etc., but is not limited thereto. The processor may include more than one processor configured to perform operations according to a model of a neural network. For example, the neural network may include a deep neural network.

[0045] Neural networks may 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), etc. The present invention relates to a neural network comprising: a neural network (CNN), a deep neural network (DLN), a deep neural network (DNN), a deep neural network (DNC ...

[0046] According to an exemplary embodiment of the present disclosure, the processor can use CNN (Convolutional Neural Network) such as GoogleNet, AlexNet, VGG network, R-CNN (Region with Convolution Neural Network), RPN (Region Proposal Network), RNN (Recurrent Neural Network), S-DNN (Stacking-based deep Neural Network), S-SDNN (State-Space Dynamic Neural Network), Deconvolution Network, DBN (Deep Belief Network), RBM (Restrcted Boltzman Machine), Fully Convolutional Network, LSTM (Long Short-Term Memory) Network, Classification Network, Generative Modeling, Explainable AI, Continuous AI, Representation Learning. Learning), Material Design AI, BERT for Natural Language Processing, SP-BERT, MRC / QA, Text Analysis, Dialog System, GPT-3, GPT-4, Visual Analytics for Visual Processing, Visual Understanding, Video Synthesis, Anomaly Detection for ResNet Data Intelligence, Prediction, Time-Series Forecasting, Optimization, Recommendation, and Data Creation, but not limited thereto. Below, the embodiments of the present disclosure will be described in detail with reference to the accompanying drawings.

[0047] Figure 1 is a diagram for explaining a device according to an embodiment of the present disclosure.

[0048] Reference Figure 1 , the device 100 can control a vehicle equipped with an ammonia fuel cell. The device 100 can predict the hydrogen consumption required for vehicle operation from a microscopic perspective based on real-time operation information and / or driving mode information. In addition, the device 100 can control a vehicle equipped with an ammonia fuel cell according to the predicted hydrogen consumption. The device 100 may include an ammonia tank 110, a cracker 120, a controller 130, and an interface 140.

[0049] The ammonia tank 110 may store ammonia and may provide the decomposer 120 with ammonia corresponding to an ammonia supply amount determined according to control of the controller 130 .

[0050] The decomposer 120 may decompose ammonia supplied from the ammonia tank 110 into hydrogen. The decomposer 120 may decompose ammonia into hydrogen under an internal condition of the decomposer 120 determined according to control of the controller 130.

[0051] The controller 130 may calculate the predicted hydrogen consumption based on at least one of the real-time operation information and the driving mode information. In addition, the controller 130 may control at least one of the ammonia supply amount and the internal condition of the decomposer based on the predicted hydrogen consumption.

[0052] Real-time operation information may refer to vehicle operation information of a user (or driver) monitored in real time from a micro perspective.

[0053] Driving pattern information may refer to information about a driver's usual driving pattern (eg, driving habits) learned by an artificial intelligence model.

[0054] The control method of the controller 130 may include a method of controlling the supply amount of ammonia supplied to the decomposer 120 and / or a method of controlling the internal conditions of the decomposer 120 .

[0055] The controller 130 may correct the hydrogen consumption for maintaining the operation state at the current time point by considering the hydrogen consumption required for the operation state predicted at a time point after the current time point.

[0056] The controller 130 may include: a memory (not shown) storing data of an algorithm or a program reproducing the algorithm for controlling the operation of components within the device 100; and at least one processor performing a control operation using the data stored in the memory. In this case, the memory and the processor may be implemented by separate chips, respectively. Alternatively, the memory and the processor may be implemented by a single chip.

[0057] The interface 140 may provide an interfacing function capable of selecting an automatic control mode for automatically controlling the ammonia supply amount or a manual control mode for manually controlling the ammonia supply amount.

[0058] Although not shown, the device 100 may further include: an input unit for inputting image information (or signals), audio information (or signals), data, or information input by a user; a communication unit including one or more components capable of communicating with an external device; an audio output unit for outputting audio data received from the outside or audio data stored in a memory, or outputting an audio signal related to a function performed by the device 100; a display unit for displaying information processed by the device 100; and a battery, etc.

[0059] The memory may store data supporting various functions of the device 100 and programs for the operation of the controller 130, may store input / output data (e.g., music files, still images, videos, etc.), and may store a plurality of application programs (applications) running in the device 100, data for the operation of the device 100, and instructions. At least a portion of these application programs may be downloaded from an external server via wireless communication. Such a memory may include at least one type of storage medium selected from the group consisting of a flash memory type, a hard disk type, an SSD type (Solid State Disk type), an SDD type (Silicon Disk Drive type), a multimedia card micro type, a card type memory (e.g., an SD or XD memory, etc.), a RAM (random access memory), an SRAM (static random access memory), a ROM (read-only memory), an EEPROM (electrically erasable programmable read-only memory), a PROM (programmable read-only memory), a magnetic memory, a magnetic disk, and an optical disk. In addition, the memory is separated from the device 100, but may also be a database connected by wire or wirelessly.

[0060] As described above, the ammonia fuel cell system is controlled by monitoring subtle driving pattern changes based on real-time operation information, the driver's existing driving pattern information, etc., thereby having the effect of stabilizing the ammonia decomposition rate of the decomposer and minimizing residual ammonia.

[0061] In addition, as described above, the amount of hydrogen produced is customized for the driver based on the driver's usual driving pattern control and information at a point in time after a specific time has passed is predicted, thereby having the effect of more accurately predicting the required hydrogen consumption and preventing energy shortage when maximum output is required.

[0062] In addition, as described above, optimizing the ammonia supply amount or the internal conditions of the decomposer has the effects of preventing the battery from being overcharged due to the ammonia supply amount exceeding the required amount, preventing the generation of residual ammonia, and stabilizing the ammonia decomposition rate.

[0063] Figure 2 is a diagram for explaining various information included in a device according to an embodiment of the present disclosure.

[0064] Reference Figure 2 , various information 200 included in the device can be stored in the memory. Various information 200 can include real-time operation information 210, driving mode information 220, first predicted hydrogen consumption information 230, second predicted hydrogen consumption information 240, appropriate ammonia supply information 250 and appropriate decomposer internal condition information 260.

[0065] The real-time operating information 210 may include, for example, one or more of the amount of pressure applied to the accelerator pedal, the amount of change in the amount of pressure applied to the accelerator pedal, the amount of pressure applied to the brake pedal, the amount of change in the amount of pressure applied to the brake pedal, the number of sudden accelerations within a preset period, the number of sudden braking within a preset period, the driving speed, the driving distance, the driving time and the driving mode, etc.

[0066] The driving mode information 220 may include, for example, one or more of an acceleration mode, a deceleration mode, a usage ratio of the driving mode, an average driving speed, an average number of lane changes, and a vehicle interior environment.

[0067] The driving modes may include one or more of, for example, comfort, economy, sport, terrain, and off-road.

[0068] Each of the first predicted hydrogen consumption information 230 and the second predicted hydrogen consumption information 240 may be generated using the real-time operation information 210 and the driving pattern information 220 .

[0069] The first predicted hydrogen consumption information 230 may be information including the first predicted hydrogen consumption at a first time point. In some embodiments, the first time point may be the current time point.

[0070] The second predicted hydrogen consumption information 240 may be information including the second predicted hydrogen consumption at a second time point. The second time point may be a time point after a predetermined time 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, this is not limited thereto.

[0071] The appropriate ammonia supply amount information 250 and / or the appropriate decomposer internal condition information 260 may be generated using the first predicted hydrogen consumption amount information 230 and the second predicted hydrogen consumption amount information 240. The appropriate ammonia supply amount information 250 and / or the appropriate decomposer internal condition information 260 may be generated by the controller 130.

[0072] The appropriate ammonia supply amount information 250 may include information indicating the amount of ammonia supplied from the ammonia tank 110 to the decomposer 120 .

[0073] The appropriate decomposer internal condition information 260 may include information indicating the internal condition of the decomposer 120. The internal condition of the decomposer 120 may include, for example, one or more of internal temperature, internal pressure, internal humidity, ammonia flow rate, hydrogen flow rate, and the like.

[0074] 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 possessed by the device 100. The environmental information of the ammonia tank 110 may be information including, for example, one or more of ammonia tank temperature, pressure, humidity, and the like.

[0075] Figure 3 is a diagram for explaining a controller according to an embodiment of the present disclosure.

[0076] Reference Figure 3 According to some embodiments, the controller 300 may control the ammonia supply amount based on the predicted hydrogen consumption. According to some embodiments, the controller 300 may include a predicted hydrogen consumption calculation unit 310 and an ammonia supply control unit 320.

[0077] The predicted hydrogen consumption calculation unit 310 may calculate multiple predicted hydrogen consumptions at a specific time point based on at least one of the real-time operation information 210 and the driving mode information 220 at each specific time point. In addition, the predicted hydrogen consumption calculation unit 310 may calculate a final predicted hydrogen consumption based on at least one of the multiple predicted hydrogen consumptions.

[0078] In some embodiments, the predicted hydrogen consumption calculation unit 310 may predict the maximum rapid acceleration state based on the real-time operation information and the driving mode information. In addition, the predicted hydrogen consumption calculation unit 310 may calculate the predicted hydrogen consumption required for stable supply of hydrogen in the maximum rapid acceleration state.

[0079] In some other embodiments, the predicted hydrogen consumption calculation unit 310 may calculate the predicted hydrogen consumption based on the real-time operation information, the driving mode information, the battery information of the device, and the environmental information of the ammonia tank.

[0080] The ammonia supply control unit 320 may calculate the ammonia supply required to ensure the final predicted hydrogen consumption. In addition, the ammonia supply control unit 320 may perform a pipeline and valve system control operation to supply ammonia equivalent to the ammonia supply to the decomposer. The pipeline and valve system control operation may refer to various control operations related to the pipeline and valve system.

[0081] The controller 300 according to some other embodiments may control the internal condition of the decomposer 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 decomposer condition control unit 330.

[0082] The decomposer condition control unit 330 may control the internal conditions of the decomposer 120 based on the decomposition amount of ammonia decomposed into hydrogen and the final predicted hydrogen consumption in the decomposer 120. For example, the decomposer condition control unit 330 may determine the temperature, pressure, humidity, flow rate, etc. inside the decomposer 120 so that the decomposition amount does not exceed or is not less than the final predicted hydrogen consumption calculated by the predicted hydrogen consumption calculation unit 310 (or so that the decomposition amount of ammonia decomposed into hydrogen in the decomposer 120 reaches the final predicted hydrogen consumption).

[0083] The controller 300 according to still other embodiments may control the ammonia supply amount and the internal condition of the decomposer 120 based on the predicted hydrogen consumption. The controller 300 according to still other embodiments may include a predicted hydrogen consumption calculation unit 310, an ammonia supply control unit 320, and a decomposer condition control unit 330.

[0084] Figure 4 is a diagram for explaining a predicted hydrogen consumption calculation unit according to one embodiment of the present disclosure.

[0085] Reference Figure 4 The predicted hydrogen consumption calculation unit 400 may include a first predicted hydrogen consumption calculation unit 410 , a second predicted hydrogen consumption calculation unit 420 and a final predicted hydrogen consumption calculation unit 430 .

[0086] The first predicted hydrogen consumption calculation unit 410 can calculate the first predicted hydrogen consumption at the first time point based on the first real-time operation information at the first time point. The first predicted hydrogen consumption calculation unit 410 can calculate the hydrogen consumption used to maintain the operation state at the first time point (e.g., the current time point). For example, the first predicted hydrogen consumption calculation unit 410 can calculate the driving speed, acceleration change rate or deceleration change rate, driving mode, operation of the air conditioning device (e.g., air conditioning, heater, etc.) at the first time point, and calculate the first predicted hydrogen consumption at the first time point.

[0087] The second predicted hydrogen consumption calculation unit 420 can calculate the second predicted 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 mode information 220. The second predicted hydrogen consumption calculation unit 420 can calculate the hydrogen consumption used to change to the predicted operating state at the second time point (for example, a time point after a specific time from the current time point). For example, the second predicted hydrogen consumption calculation unit 420 can predict the operating state at the second time point using an artificial intelligence model that learns the driver's existing driving mode information. Specifically, for example, the second predicted hydrogen consumption calculation unit 420 can predict the driver's operating state at the second time point based on the first real-time operation information at the first time point using an artificial intelligence model that learns the driver's existing driving mode information. In addition, the second predicted hydrogen consumption calculation unit 420 can analyze the difference between the first time point and the second time point, and calculate the second predicted hydrogen consumption corresponding to the change in the predicted hydrogen consumption.

[0088] The final predicted hydrogen consumption calculation unit 430 may calculate the final predicted hydrogen consumption based on at least one of the first predicted hydrogen consumption and the second predicted hydrogen consumption. In some embodiments, the final predicted hydrogen consumption calculation unit 430 may calculate the final predicted hydrogen consumption using the first predicted hydrogen consumption and the second predicted hydrogen consumption.

[0089] Figure 5 It is a diagram for explaining an operating method of a device according to an embodiment of the present disclosure.

[0090] Reference Figure 1 and Figure 5 The operating method of the device 100 may be a method for controlling a vehicle equipped with an ammonia fuel cell.

[0091] In step S100, the apparatus 100 may calculate the predicted hydrogen consumption based on at least one of the real-time operation information and the driving mode information. Figure 3 The predicted hydrogen consumption calculation unit 310 and Figure 4 The predicted hydrogen consumption calculation unit 400 is executed.

[0092] In step S200, the apparatus 100 may control at least one of the amount of ammonia supplied from the ammonia tank 110 and the internal conditions of the decomposer 120 that decomposes the ammonia supplied from the ammonia tank 110 into hydrogen based on the predicted hydrogen consumption. An embodiment of step S200 may be Figure 1 Controller 130, Figure 3 Ammonia supply control unit 320 and / or Figure 3 The decomposer condition control unit 330 executes.

[0093] On the other hand, the embodiments of the present disclosure may be implemented in the form of a recording medium storing instructions executable by a computer. The instructions may be stored in the form of program code, and when the instructions are executed by a processor, a program module may be generated to perform the operations of the disclosed embodiments. The recording medium may be implemented in the form of a computer-readable recording medium.

[0094] Computer-readable recording media include all types of recording media that store instructions that can be read by a computer, such as ROM (Read Only Memory), RAM (Random Access Memory), magnetic tapes, magnetic disks, flash memory, optical data storage devices, etc.

[0095] Above, the disclosed embodiments are described with reference to the accompanying drawings. Those skilled in the art to which the present disclosure belongs will understand that the present disclosure can be implemented in a form different from the disclosed embodiments without changing the technical ideas or essential features of the present disclosure. The disclosed embodiments are exemplary and should not be interpreted as limiting.

Claims

1. A device for controlling a vehicle equipped with an ammonia fuel cell, the device comprising: Ammonia tank, to store ammonia; a decomposer for decomposing ammonia supplied from the ammonia tank into hydrogen; as well as A controller 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 an internal condition of the decomposer based on the predicted hydrogen consumption amount.

2. The device according to claim 1, wherein: The controller comprises: a predicted hydrogen consumption calculation unit that calculates a plurality of predicted hydrogen consumptions at a specific time point based on at least one of the real-time operation information and the driving mode information at each specific time point, and calculates a final predicted hydrogen consumption based on at least one of the plurality of predicted hydrogen consumptions; and An ammonia supply amount control unit calculates the ammonia supply amount required to ensure the final predicted hydrogen consumption amount, and performs a pipe and valve system control operation to supply ammonia equivalent to the ammonia supply amount to the decomposer.

3. The device according to claim 1, wherein: The controller comprises: a predicted hydrogen consumption calculation unit that calculates a plurality of predicted hydrogen consumptions at a specific time point based on at least one of the real-time operation information and the driving mode information at each specific time point, and calculates a final predicted hydrogen consumption based on at least one of the plurality of predicted hydrogen consumptions; and A cracker condition control unit controls an internal condition of the cracker based on a decomposition amount of the ammonia into the hydrogen in the cracker and the final predicted hydrogen consumption.

4. The device according to claim 1, wherein: The controller comprises: a predicted hydrogen consumption calculation unit, which calculates a plurality of predicted hydrogen consumptions at a specific time point based on at least one of the real-time operation information and the driving mode information at each specific time point, and calculates a final predicted hydrogen consumption based on at least one of the plurality of predicted hydrogen consumptions; an ammonia supply amount control unit that calculates the ammonia supply amount required to ensure the final predicted hydrogen consumption amount and performs a pipeline and valve system control operation to supply ammonia equivalent to the ammonia supply amount to the decomposer; and The decomposer condition control unit sets the internal conditions of the decomposer so that the decomposition amount of the ammonia into the hydrogen in the decomposer reaches the final predicted hydrogen consumption.

5. The device according to claim 4, wherein: The predicted hydrogen consumption calculation unit comprises: a first predicted hydrogen consumption calculation unit, which calculates a first predicted hydrogen consumption at a first time point based on first real-time operation information at the first time point; a second predicted hydrogen consumption calculation unit that calculates a second predicted hydrogen consumption at a second time point later than the first time point based on at least one of the real-time operation information and the driving mode information; and The final predicted hydrogen consumption calculation unit calculates the final predicted hydrogen consumption based on at least one of the first predicted hydrogen consumption and the second predicted hydrogen consumption.

6. The device according to claim 5, wherein: The second predicted hydrogen consumption calculation unit uses an artificial intelligence model that learns the driver's existing driving pattern information to predict the driver's operating status at the second time point based on the first real-time operating information at the first time point, analyzes the difference between the first time point and the second time point, and calculates the second predicted hydrogen consumption corresponding to the change in predicted hydrogen consumption.

7. The device according to claim 4, wherein: The predicted 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 predicted hydrogen consumption required for stably supplying the hydrogen in the maximum rapid acceleration state.

8. The device according to claim 4, wherein: The predicted hydrogen consumption calculation unit calculates the predicted 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.

9. The device according to claim 1, wherein: The real-time operation information includes at least one of the pressure applied to the accelerator pedal, the change in the pressure applied to the accelerator pedal, the pressure applied to the brake pedal, the change in the pressure applied to the brake pedal, the number of sudden accelerations within a preset period, the number of sudden braking within the preset period, the driving speed, the driving distance, the driving time, and the driving mode. The driving mode information includes one or more of an acceleration mode, a deceleration mode, a usage ratio of the driving mode, an average driving speed, an average number of lane changes, and an internal environment of the vehicle.

10. An operating method, the operating method being an operating method of a device for controlling a vehicle equipped with an ammonia fuel cell, the operating method comprising: the step of calculating a predicted hydrogen consumption based on at least one of the real-time operating information and the driving pattern information; as well as A step of controlling at least one of an amount of ammonia supplied from an ammonia tank and an internal condition of a decomposer that decomposes the ammonia supplied from the ammonia tank into hydrogen based on the predicted hydrogen consumption.

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