Fuel Cell Shutdown Wake-Up Control for Freeze Risk Prevention
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Solution Overview
Problem
Current fuel cell systems lack a comprehensive and accurate method to determine self-wake-up intervals and control strategies post-shutdown, failing to account for ambient temperature changes, which can lead to reduced durability and reliability due to freezing or liquid water damage.
Innovation Solution
A self-wake-up control method for fuel cell systems that estimates ambient temperature using remote data from a cloud platform and local vehicle data, determining optimal wake-up intervals and modes based on temperature variations of system components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a timed wake-up method is adopted (e.g., waking up once every hour or every few hours), then the wake-up process is simple to implement, but overly frequent wake-ups waste energy while excessively long intervals fail to achieve the desired monitoring purpose
Solution Approach 1:
The system performs preliminary actions by predicting future ambient temperature changes before shutdown occurs. Based on historical weather data and shutdown timing, the system determines in advance whether freezing conditions are likely, and pre-configures the wake-up schedule accordingly. This eliminates the need for frequent routine wake-ups when freezing is not expected, while ensuring wake-ups are scheduled in advance when cold conditions are predicted.
Solution Approach 2:
The wake-up interval is made dynamic rather than fixed. The system adjusts the wake-up frequency based on predicted ambient temperature conditions - extending intervals when temperatures remain stable and above freezing, while shortening intervals when cold snaps are forecasted. This dynamic adjustment optimizes energy consumption while maintaining adequate monitoring coverage.
2Ease of operation
If a timed wake-up method is adopted (e.g., waking up once every hour or every few hours), then the wake-up control is easy to implement, but excessively long intervals fail to achieve the desired monitoring purpose
Solution Approach 1:
The system performs preliminary actions by predicting future ambient temperature changes before shutdown occurs. Based on historical weather data and shutdown timing, the system determines in advance whether freezing conditions are likely, and pre-configures the wake-up schedule accordingly. This eliminates the need for frequent routine wake-ups when freezing is not expected, while ensuring wake-ups are scheduled in advance when cold conditions are predicted.
Solution Approach 2:
The system incorporates feedback mechanisms where actual temperature measurements during wake-ups are compared against predictions. This feedback loop allows the system to learn from past performance and improve future predictions, ensuring that wake-up intervals remain effective for monitoring while optimizing energy consumption. The feedback ensures reliability by confirming that monitoring gaps do not miss critical temperature events.
3Reliability
If the system does not accurately confirm ambient temperature, then the control strategy cannot be optimized, but implementing comprehensive temperature monitoring increases system complexity
Solution Approach 1:
The system introduces an intermediary approach by using a combination of sparse physical temperature sensors and computational weather prediction models. Rather than deploying extensive sensor networks throughout the system, the invention uses limited sensor data combined with external meteorological data and predictive algorithms to infer ambient temperature conditions. This intermediary computational layer reduces hardware complexity while maintaining reliable temperature awareness for control decisions.
Solution Approach 2:
The invention substitutes mechanical/sensor-based temperature monitoring with a computational prediction system. Instead of relying solely on physical sensors and complex monitoring infrastructure, the system uses algorithms that process weather data, historical patterns, and shutdown conditions to predict temperature evolution. This substitution reduces device complexity by replacing extensive physical monitoring systems with intelligent computational models.
Data Source
AI summary
A self-wake-up control method for a fuel cell system after shutdown, a control unit, a computer program product, and a storage medium are disclosed. The self-wake-up control method for a fuel cell system after shutdown includes (i) an ambient temperature estimation and confirmation step, wherein, in response to shutdown of the fuel cell system, remote data related to the driving of the fuel cell vehicle is acquired from outside the fuel cell vehicle, and local data related to the driving of the fuel cell vehicle is acquired from the fuel cell vehicle, and, based on the remote data and the local data, the ambient temperature of the fuel cell system is estimated and confirmed, and (ii) and a self-wake-up process determination step, wherein, based on the confirmed ambient temperature and in combination with data related to temperature variations of relevant components of the fuel cell system, a corresponding wake-up time interval and wake-up mode are determined. This can effectively eliminate the freezing risk of the fuel cell system and enhance the environmental adaptability, reliability, and durability of the fuel cell vehicle.