Hydrogen Refueling Data Exchange for Demand Forecasting
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Solution Overview
Problem
Current hydrogen refueling stations lack efficient data exchange with vehicles, leading to excessive hydrogen storage, high energy expenditure, and increased hydrogen prices, which negatively impacts consumer acceptance of hydrogen-powered vehicles.
Innovation Solution
A computer-implemented method for data exchange between refueling stations and clients, enabling the transmission of refueling requests and generating refueling proposals that differ in parameters such as refueling time, price, and energy type, allowing for optimized hydrogen production and treatment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If refueling stations store large amounts of hydrogen prophylactically, then hydrogen availability is ensured, but energy expenditure increases and hydrogen prices rise
Solution Approach 1:
The system performs preliminary actions by storing hydrogen in advance during periods of low demand and low production costs. The forecasted demand data is used to determine optimal storage quantities before peak demand periods, allowing stations to have hydrogen ready without excessive storage capacity. This resolves the contradiction by enabling reliable availability through strategic pre-storing rather than continuous over-storing.
Solution Approach 2:
The hydrogen storage strategy becomes dynamic through continuous adjustment based on real-time and historical demand data. The system adapts storage levels, production schedules, and pricing dynamically according to forecasted patterns, seasonal variations, and current market conditions. This dynamic approach allows optimization of energy expenditure while maintaining reliability through flexible response to changing conditions.
2Use of energy by stationary object
If refueling stations minimize prophylactic hydrogen storage to save energy, then energy expenditure decreases, but waiting times increase and hydrogen may not be available
Solution Approach 1:
The system addresses waiting time issues by performing preliminary hydrogen production and storage during off-peak hours when demand is low. The forecasted demand patterns enable the system to prepare hydrogen in advance, ensuring availability when customers need it without requiring excessive continuous storage. This resolves the time contradiction by strategic pre-preparation rather than continuous readiness.
Solution Approach 2:
The system maintains continuity of useful action through continuous hydrogen production cycles that are optimized based on forecasted demand. Rather than intermittent production, the system operates continuously at optimized rates, constantly producing and preparing hydrogen to meet predicted demand patterns. This continuous operation ensures availability while minimizing energy expenditure through efficient, demand-driven production cycles.
3Productivity
If hydrogen is produced and stored quickly to meet demand, then availability is improved, but energy expenditure increases
Solution Approach 1:
The hydrogen production rate becomes dynamic rather than constant, adjusting continuously based on forecasted demand patterns, seasonal variations, and real-time conditions. The system optimizes production intensity to match predicted consumption, producing at higher rates when demand forecasts indicate need and reducing rates during low-demand periods. This dynamic production approach improves productivity when needed while minimizing overall energy expenditure through demand-aligned production scheduling.
Solution Approach 2:
The system utilizes parameter changes in production rates, storage temperatures, and compression levels to optimize the balance between productivity and energy expenditure. By varying these parameters dynamically based on forecasted demand and current conditions, the system achieves high productivity during critical periods without sustained high energy consumption. This parameter optimization resolves the contradiction by flexible adjustment rather than constant high-performance operation.
Data Source
AI summary
A method for data exchange between a refueling station and a client, including: transmitting a refueling request from the client to the refueling station via wireless data transmission, receiving at least two refueling proposals from the refueling station, wherein the at least two transmitted refueling proposals differ from one another in at least one proposed refueling parameter, selected from the group of: refueling time, refueling duration, maximum filling amount, maximum filling speed, energy required for refueling on the part of the refueling station, price of hydrogen, waiting time before refueling, type of energy used to produce the hydrogen to be refueled, CO2 certificate, environmental certificate. Also disclosed is a method for controlling a refueling process of a vehicle, for detecting a client consumption pattern or a client refueling pattern, for controlling hydrogen production or hydrogen treatment for refueling at least one vehicle, a controller, a system, and a remote server.