Hydrogen Tank Refilling Heat Capacity Model
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Solution Overview
Problem
Current hydrogen tank refueling methods are conservative due to unknown parameters, leading to inefficiencies and increased safety margins, resulting in longer fill times, lower final pressures, and higher energy usage, with no method to adjust for deviations from standard operating conditions, affecting customer satisfaction and station efficiency.
Innovation Solution
The MC Method introduces a new tank filling model based on total heat capacity and an advanced algorithm that considers heat transfer to improve hydrogen filling station performance across a range of conditions, allowing for more efficient and accurate refueling operations by predicting end-of-fill temperature and pressure without requiring full-communication protocols.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conservative refueling procedures are used to ensure safety, then safety margins are improved, but fill time increases and productivity decreases
Solution Approach 1:
The system dynamically changes refueling parameters (pressure ramp rate, pre-cooling temperature, fill mass) based on real-time tank conditions and heat capacity calculations, replacing static conservative parameters with adaptive optimized parameters that maintain safety while improving fill speed
Solution Approach 2:
The system uses feedback from temperature sensors, pressure measurements, and heat capacity modeling to continuously adjust refueling parameters during the process, enabling real-time optimization that balances safety constraints with productivity goals
2Reliability
If conservative refueling procedures are used to ensure safety, then safety margins are improved, but energy consumption increases
Solution Approach 1:
The system optimizes pre-cooling temperature and fill mass parameters based on calculated heat capacity, avoiding excessive energy expenditure on pre-cooling while maintaining safety margins through dynamic parameter adjustment
Solution Approach 2:
The system replaces mechanical/thermal trial-and-error approaches with thermodynamic modeling and heat capacity calculations to predict optimal refueling parameters, reducing energy waste through physics-based optimization
3Reliability
If lookup tables with conservative assumptions are used, then reliability is improved, but measurement precision of actual tank conditions deteriorates
Solution Approach 1:
The system replaces static lookup tables with dynamic thermodynamic modeling that calculates actual tank heat capacity and predicts temperature/pressure evolution, providing precise assessment of real tank conditions rather than relying on conservative assumptions
Solution Approach 2:
The system segments the refueling process into discrete phases (pre-cooling, filling, post-fill) with specific measurements and calculations for each phase, enabling precise tracking of actual tank conditions throughout the process
4Reliability
If pre-cooling temperature is maintained at design set point, then safety is improved, but device complexity and cost increase
Solution Approach 1:
The system transitions from static pre-cooling temperature maintenance to dynamic adjustment of pre-cooling parameters based on real-time heat capacity calculations and predicted tank conditions, allowing flexible adaptation without over-engineering the cooling system
Solution Approach 2:
The system changes pre-cooling temperature parameters dynamically based on calculated heat capacity and ambient conditions, allowing the station to operate effectively with simpler cooling infrastructure by optimizing parameters rather than over-designing hardware
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The MC Method enhances fill speed and quality, reduces costs, and maintains safety margins, enabling hydrogen stations to operate efficiently and effectively under various conditions while maintaining customer satisfaction.
Implementation Method 1
an advanced algorithm based on that model for improving the performance of hydrogen filling stations under a broad range of operating conditions and, in particular, includes additional consideration of heat transfer to the hydrogen from downstream components
Data Source
AI summary
Disclosed is an improved analytical method that can be utilized by hydrogen filling stations for directly and accurately calculating the end-of-fill temperature in a hydrogen tank that, in turn, allows for improvements in the fill quantity while tending to reduce refueling time. The calculations involve calculation of a composite heat capacity value, MC, from a set of thermodynamic parameters drawn from both the tank system receiving the gas and the station supplying the gas. These thermodynamic parameters are utilized in a series of simple analytical equations to define a multi-step process by which target fill times, final temperatures and final pressures can be determined. The parameters can be communicated to the station directly from the vehicle or retrieved from a database accessible by the station. Because the method is based on direct measurements of actual thermodynamic conditions and quantified thermodynamic behavior, significantly improved tank filling results can be achieved.