AI Controller for Hydrogen Refueling Station Operations
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Solution Overview
Problem
Existing hydrogen refueling stations face challenges in efficiently managing and maintaining operations, particularly when operators lack experience or unexpected situations arise, leading to inefficient refueling and potential equipment malfunctions.
Innovation Solution
A device and method for controlling hydrogen refueling that utilizes artificial intelligence to collect and analyze data from hydrogen refueling facilities, predict optimal refueling times, automate inspections, and systematize operations, ensuring seamless and efficient refueling processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual operation and monitoring of hydrogen refueling facilities is used, then operational flexibility and adaptability are maintained, but operational efficiency and reliability deteriorate due to operator inexperience and inability to handle unexpected situations
Solution Approach 1:
The control system performs self-diagnosis and self-correction by automatically detecting malfunctions, generating maintenance notifications, and executing control adjustments without requiring operator intervention. The system monitors its own operational status and manages itself through automated protocols.
Solution Approach 2:
Manual operational control is replaced with an automated control system that uses sensors, processors, and communication modules to monitor and regulate hydrogen refueling operations. The mechanical/manual control mechanism is substituted with an intelligent electronic control system.
2Productivity
If automated control systems are implemented to improve operational efficiency, then productivity increases, but the ability to handle unexpected situations and adapt to different conditions deteriorates
Solution Approach 1:
The system continuously monitors operational parameters through sensors and adjusts control actions based on real-time feedback. When anomalies are detected, the system receives feedback signals and automatically modifies its operation to handle unexpected situations, maintaining both efficiency and adaptability.
Solution Approach 2:
The control system transitions from static predetermined operations to dynamic adaptive control. The system can change its operational parameters and response strategies in real-time based on detected conditions, allowing it to adapt to unexpected situations while maintaining high productivity.
3Reliability
If continuous monitoring and inspection of facilities is performed to maintain reliability, then system reliability improves, but operational time and cost increase
Solution Approach 1:
The system performs preliminary inspections and maintenance actions before actual failures occur. By continuously monitoring and detecting potential issues early, the system can schedule maintenance during low-demand periods, minimizing impact on operational time while ensuring high reliability.
Solution Approach 2:
The monitoring system operates continuously without interrupting refueling operations. Inspections and maintenance activities are scheduled during off-peak hours or performed remotely, allowing continuous monitoring while minimizing time loss during actual maintenance operations.
4Ease of manufacture
If manual inspection and maintenance scheduling is used, then operational simplicity is maintained, but maintenance quality and timeliness deteriorate
Solution Approach 1:
Manual inspection and scheduling processes are replaced with automated systems that use sensors, data analysis, and intelligent algorithms to determine maintenance needs. The mechanical/manual scheduling mechanism is substituted with an automated system that provides precise, data-driven maintenance timing and sequencing.
Data Source
AI summary
A device for controlling refueling of hydrogen includes one or more hydrogen refueling facilities for refueling a hydrogen-powered vehicle with hydrogen, and a controller that collects state information and control data of the hydrogen refueling facilities, performs artificial intelligence learning based on the state information and the control data, and controls operations of the hydrogen refueling facilities based on the learning result.


