Fuel Cell Shutdown Control via Learning Functions
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Solution Overview
Problem
Fuel cell systems in vehicles face inefficiencies in shutdown procedures, leading to frequent start-stop processes that increase wear and aging of components, with existing methods not adequately accounting for vehicle operation patterns or energy storage states.
Innovation Solution
Implementing learning functions to determine the number and duration of vehicle stop phases, delaying fuel cell system shutdowns, and adjusting the state of charge of alternative energy storage, while minimizing air supply subsystem operations through strategic use of bypass flaps and navigation data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If the fuel cell system is shut down during vehicle parking, then energy consumption is reduced, but frequent start-stop processes increase wear and aging of components
Solution Approach 1:
The system performs preliminary actions by determining the number and duration of stop phases in advance using learning functions, and prepares the fuel cell system accordingly. The control unit decides whether to maintain operation or shut down based on predicted stop duration, preventing unnecessary shutdowns that would cause wear while still saving energy when appropriate.
Solution Approach 2:
The system incorporates feedback mechanisms by continuously monitoring vehicle operation patterns, stop phase durations, and component states. The control unit uses this feedback to dynamically adjust shutdown decisions, learning from historical data to optimize the balance between energy savings and component protection.
2Reliability
If the air supply subsystem remains active during stop phases, then component wear is reduced, but energy consumption increases
Solution Approach 1:
The air supply subsystem operates dynamically with variable characteristics. During stop phases, the system can maintain operation with reduced air mass flow by opening bypass flaps and reducing compressor load, rather than complete shutdown. This dynamic operation mode reduces wear while minimizing energy consumption during parking periods.
3Productivity
If learning functions are implemented to determine stop phases, then shutdown timing is optimized, but system complexity increases
Solution Approach 1:
The control unit performs self-service by automatically determining stop phases and optimizing shutdown timing using learning functions embedded in its software. The system learns from historical operation patterns and autonomously makes decisions about fuel cell shutdown timing without requiring external intervention or complex additional hardware components.
4Reliability
If the fuel cell system operates during short stop phases, then component wear is minimized, but unnecessary energy consumption occurs
Solution Approach 1:
The system changes operational parameters dynamically based on stop phase duration. For short stop phases, the fuel cell continues operating at reduced load. For longer stop phases, the system transitions to shutdown mode. The control unit uses learned patterns to identify the threshold between short and long stops, optimizing the parameter changes to minimize both wear and energy waste.
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
This approach reduces the number of start-stop processes, minimizing load on actuators, extending component lifespan, and reducing noise emissions, leading to more robust and efficient fuel cell system operation.
Implementation Method 1
a fuel cell system (22) and at least one alternative energy store (26), wherein the energy for a drive train (12) of the vehicle (10) can be obtained both from the fuel cell system (22) and from the at least one alternative energy store (26)
Implementation Method 2
a subsystem (30) for conveying air
Data Source
Figure 1
Figure 2
AI summary
The invention relates to a method and to a system for operating a fuel cell system (22) and at least one sub-system (30) of the fuel cell system (22). According to the invention, these are arranged in a vehicle (10), wherein the energy for a drive train (12) of the vehicle (10) can be drawn both from the fuel cell system (22) and from an alternative energy store (26). The method comprises the following method steps: first, the number and duration of shut-down and/or stop phases of the vehicles (10) in a defined time interval in a first vehicle state (86) or in a second vehicle state (88) is determined based on vehicle state-specific learning functions (90, 112). Operating parameters of the fuel cell system (22) and of the at least one sub-system (30) of the fuel cell system (22) are then adjusted in dependence on the determined number and duration of shut-down and/or stop phases of the vehicle (10).