Fuel Battery Learning System Impedance Update Control
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Solution Overview
Problem
Existing battery learning systems face inaccuracies in updating output characteristic curves due to the use of apparent impedance values when true impedance values are not obtained, leading to improper updates and decreased accuracy.
Innovation Solution
A battery learning system that acquires actual characteristic values and impedance values, with a judgment mechanism to prohibit updates if the impedance value acquisition interval exceeds a threshold, ensuring accurate updating of output characteristic curves by using activation overvoltage calculations and prohibiting updates based on unacquired impedance values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the output characteristic curve is updated using apparent impedance values, then the updating process can be performed continuously, but the accuracy of the output characteristic curve decreases due to improper updates when true impedance values are not obtained
Solution Approach 1:
The system performs preliminary checks to determine whether true impedance values are available before initiating the update process. By checking the availability of accurate impedance data in advance, the system prevents improper updates from occurring, thereby maintaining curve accuracy without sacrificing updating frequency when conditions are favorable.
Solution Approach 2:
The update mechanism dynamically adjusts its behavior based on the availability of true impedance values. When true impedance values are available, the system performs updates; when only apparent impedance values are available, the system refrains from updating. This dynamic approach allows the system to optimize between updating frequency and accuracy based on real-time conditions.
2Measurement precision
If impedance value acquisition is performed continuously, then the accuracy of output characteristic curve updates is maintained, but the system complexity and resource consumption increase
Solution Approach 1:
The system applies different quality standards to different impedance value acquisitions. True impedance values, which are harder to obtain but more accurate, are used selectively when available. Apparent impedance values, which are easier to obtain but less accurate, are used only when true values are unavailable. This local quality approach balances accuracy requirements with system complexity.
Solution Approach 2:
The system changes the parameter of impedance value quality based on availability. Instead of continuously acquiring high-precision impedance values which would increase system complexity, the system accepts varying qualities of impedance data (true vs. apparent) depending on what can be obtained, thereby maintaining acceptable accuracy without excessive complexity.
3Stability of the object's composition
If updates are performed using apparent impedance values when true values are not available, then the output characteristic curve can be maintained, but incorrect updates occur leading to decreased reliability
Solution Approach 1:
The system takes preliminary anti-action by preventing updates when true impedance values are not available. Instead of allowing potentially incorrect updates to occur, the system proactively blocks the update process under inappropriate conditions. This prevents the accumulation of errors in the output characteristic curve while maintaining system reliability.
Solution Approach 2:
The system uses feedback from the impedance value acquisition status to control the update process. By monitoring whether true impedance values are available and using this information to决定是否 perform updates, the system ensures that updates only occur when reliable data is present, thereby maintaining both continuity and reliability of the output characteristic curve.
Data Source
AI summary
A fuel battery system is comprised of a power source circuit, a rotating electrical machine that is a load, a memory device and a control unit. Here, a battery learning system corresponds to an arrangement including a fuel battery that is a structural component of the power source circuit, a high frequency signal source, an electric current detection means, a voltage detection means, the memory device and a battery learning part that is a structural element of the control unit. An impedance value can be obtained from alternating current components of respective detecting values of the electric current detection means and the voltage detection means. The battery learning unit has an I-V characteristic curve learning module that learns an I-V characteristic curve and a learning prohibition judgment module that judges whether or not an acquiring interval of the impedance value is over a predetermined threshold interval set in advance, and prohibits learning if the former is over the latter.