AI Power Supply Controller Optimizing Standby Energy Efficiency
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Solution Overview
Problem
Power supply systems face challenges in maintaining optimal energy conversion efficiency due to manufacturing variations and environmental changes, such as thermal effects and component wear, which affect performance metrics like efficiency, noise, and emissions.
Innovation Solution
A power supply system incorporating a system performance controller that uses an artificial intelligence algorithm to monitor and adjust control parameters, such as zero-voltage switching (ZVS) parameters, switching frequency, and duty cycle, to optimize energy conversion efficiency by computing efficiency metrics in real-time and making adjustments based on measured conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If manufacturing variations and environmental changes are not compensated, then the system design is simple, but the energy conversion efficiency and performance metrics deteriorate
Solution Approach 1:
The patent implements a feedback mechanism where a metering circuit continuously measures actual operating conditions (input voltage, output voltage, input current, output current) and feeds this information to a system performance controller. The controller computes energy conversion efficiency in real-time and adjusts control parameters accordingly, creating a closed-loop system that compensates for manufacturing variations and environmental changes, thereby maintaining optimal energy conversion efficiency despite the added system complexity.
Solution Approach 2:
The system dynamically changes control parameters (such as switching frequency, duty cycle, or phase shift) based on computed energy conversion efficiency and measured operating conditions. The system performance controller modifies these parameters iteratively using AI algorithms to optimize efficiency, allowing the system to adapt to manufacturing variations and environmental changes without requiring a completely complex redesign.
2Loss of energy
If control parameters are adjusted frequently to maintain efficiency, then energy conversion efficiency is optimized, but the system response time and complexity increase
Solution Approach 1:
The system performs preliminary computation of energy conversion efficiency based on measured conditions before making control parameter adjustments. The AI algorithm predicts optimal parameter values in advance, and the system prepares adjustment commands beforehand, reducing the actual response time when efficiency deviations are detected. This preliminary action allows the system to maintain optimized efficiency without excessive response delays.
3Loss of energy
If AI algorithms are used to optimize control parameters, then energy conversion efficiency achieves threshold conditions, but the computational complexity and processing requirements increase
Solution Approach 1:
The system implements self-service through autonomous AI algorithms that automatically compute optimal control parameters without external intervention. The system performance controller continuously monitors measured conditions, computes energy conversion efficiency, and independently adjusts control parameters to maintain efficiency above threshold conditions. This self-service capability reduces the need for complex external control systems while achieving optimized energy conversion efficiency.
Data Source
AI summary
According to an aspect, a power supply system includes a power stage, a power supply controller configured to control operations of the power stage, a metering circuit configured to sense measured conditions of the power stage, and a system performance controller configured to be coupled to the power supply controller and the metering circuit. The system performance controller is configured to set or adjust a control parameter for the power stage based on standby power of the power stage. The system performance controller includes a standby power computation circuit configured to compute the standby power of the power stage based on the measured conditions, and a control manipulation module configured to modify the control parameter until the standby power achieves a threshold condition.