Fuel Cell Anode Purge Valve Control for Variable Hydrogen Management
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Solution Overview
Problem
Conventional methods for controlling anode purge valves in fuel cell systems struggle to adapt flexibly to variable working conditions, leading to reduced hydrogen utilization rates and system instability due to nitrogen accumulation and excessive hydrogen discharge.
Innovation Solution
Implementing a neural network model based on reinforcement learning to control the anode purge valve, utilizing a twin delayed deep deterministic policy gradient algorithm (TD3) to optimize hydrogen utilization and nitrogen management under dynamic conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If cyclic exhausting with fixed purge valves is used, then steady state working needs are met, but flexible adjustment under variable working conditions cannot be made
Solution Approach 1:
The patent transforms the fixed, static purge valve control into a dynamic control system using reinforcement learning. The neural network model continuously adapts purge valve timing and duration based on real-time system state inputs (current, temperature, pressure), enabling flexible adjustment to variable working conditions while maintaining stability through learned optimal policies.
Solution Approach 2:
The system dynamically changes control parameters (purge valve opening time, purge frequency) based on operating conditions. The reinforcement learning model adjusts these parameters in real-time according to system state, moving from fixed parameters to adaptive parameter control that responds to changing load, temperature, and pressure conditions.
2Reliability
If shorter exhaust intervals are used to prevent fuel shortage, then fuel supply is ensured, but large amount of hydrogen is discharged directly while not reacting
Solution Approach 1:
The reinforcement learning control system implements continuous feedback by monitoring system state (current, temperature, pressure) and adjusting purge valve operation accordingly. This feedback mechanism allows the system to determine optimal purge timing and duration, ensuring fuel supply while minimizing unnecessary hydrogen discharge through data-driven decision making.
Solution Approach 2:
The system uses reinforcement learning to enable self-optimization of purge operations. Through continuous interaction with the environment and reward-based learning, the system autonomously learns to balance fuel supply requirements against hydrogen utilization efficiency, making intelligent decisions without external intervention.
3Ease of operation
If fixed working intervals and working times are used for purge valves, then simple control is achieved, but hydrogen utilization rate decreases due to excessive discharge
Solution Approach 1:
The patent replaces traditional mechanical/timer-based fixed interval control with an intelligent software-based reinforcement learning system. The neural network model processes system state inputs and generates optimized purge control signals, substituting simple timer mechanics with adaptive intelligent control that improves hydrogen utilization while maintaining operational simplicity through automated decision making.
Data Source
AI summary
This application provides a method for controlling an anode purge valve of a fuel cell, a device, a medium, and a product, and relates to the field of fuel cell control technologies. The method includes: acquiring a system state of a fuel cell system and a corresponding reward value; inputting the system state of the fuel cell system and the corresponding reward value into a trained prediction model, to obtain a control action; the trained prediction model is a neural network model based on a reinforcement learning algorithm; and controlling an anode purge valve of the fuel cell system based on the control action. In this application, the reinforcement learning technology is introduced into the control of the anode purge valve of the fuel cell.