Hydrogen Fueling Control Using Vehicle-Side Tank State Feedback
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Solution Overview
Problem
Conventional hydrogen fueling processes for hydrogen fueled mobility lack efficiency, speed, and real-time operability, and do not effectively utilize state-of-the-art information and communication technologies for precise control and validation based on real-time on-site data.
Innovation Solution
A hydrogen fueling test method and system that utilizes an artificial neural network model to predict and control the state of hydrogen in a mobility tank based on real-time data, integrating feedback and simulation results to optimize temperature and pressure conditions for safe and rapid fueling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional hydrogen fueling processes are used, then safety control is maintained, but fueling efficiency and speed are insufficient
Solution Approach 1:
The patent implements a closed-loop feedback control system that continuously monitors temperature, pressure, and flow rate during hydrogen fueling. Real-time data from sensors is fed back to the control unit, which dynamically adjusts fueling parameters to optimize speed while maintaining safety limits, resolving the contradiction between fueling efficiency and safety control
Solution Approach 2:
The system transitions from static safety limits to dynamic adaptive control. The control unit continuously adjusts fueling rate, temperature, and pressure based on real-time system state and predicted future conditions, enabling both high fueling speed and continuous safety maintenance through dynamic parameter optimization
2Measurement precision
If real-time data collection and model updating are implemented, then fueling control precision is improved, but system complexity increases
Solution Approach 1:
The control system performs self-updating by automatically collecting operational data, processing it through the neural network model, and refining its own control parameters without external intervention. This self-service capability achieves high control precision while minimizing the need for complex external calibration systems
Solution Approach 2:
The patent replaces complex mechanical calibration and adjustment systems with an intelligent software-based neural network model. The model learns optimal control strategies from data, substituting physical complexity with computational intelligence to achieve precise control
3Speed
If neural network models are used for prediction, then fueling speed and real-time operability are enhanced, but computational requirements and processing time increase
Solution Approach 1:
The neural network model is trained offline in advance with extensive fueling data to learn optimal control patterns. During actual fueling operations, the pre-trained model provides rapid predictions and control recommendations, eliminating the need for complex real-time computations and enabling both high speed and low processing time
Data Source
AI summary
A method according to one embodiment of the present disclosure comprises the steps of: transmitting a control request regarding the state of hydrogen in a mobility tank of a hydrogen vehicle to the hydrogen vehicle; obtaining on-site data on the change in the state of hydrogen in the mobility tank as feedback in response to the control request; and updating a model of the change in the state of hydrogen in the mobility tank in response to the control request based on the on-site data of the change in the state of hydrogen in the mobility tank in response to the control request.