AI-Based MPPT Sampling Ratio Control for Energy Harvesting
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Solution Overview
Problem
Existing maximum power point tracking (MPPT) algorithms, such as perturbation and observation (P&O), are inefficient due to quantization errors and misjudgment of minimum power points as maximum power points, especially when operating at 80% of open circuit voltage, leading to suboptimal power extraction in energy harvesting systems.
Innovation Solution
A method utilizing an artificial intelligence (AI) algorithm with an accelerated calculation function to estimate and adjust the sampling ratio between 50% to 90% of the open circuit voltage, allowing for real-time tracking of maximum power points by calculating input power and controlling the resistor divider ratio to optimize sampling operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing MPPT algorithms (e.g., P&O) are used to track maximum power, then the system can operate continuously, but quantization errors and misjudgment of power points lead to reduced measurement precision and suboptimal power extraction
Solution Approach 1:
The patent implements dynamic adjustment of the sampling ratio based on real-time power conditions. The sampling ratio is not fixed but varies according to the operating point, allowing the system to adapt to changing conditions and maintain high precision across different power levels. This dynamic approach resolves the contradiction by making the measurement precision adaptable rather than static.
Solution Approach 2:
The patent changes the sampling ratio parameter dynamically based on the detected power level and trends. By adjusting this key parameter, the system optimizes the balance between sampling accuracy and processing efficiency, directly addressing the measurement precision issue while maintaining productivity.
2Productivity
If a fixed sampling ratio (e.g., 80% of open circuit voltage) is used for MPPT, then the algorithm is simple to implement, but it cannot adapt to varying environmental conditions and fails to extract maximum power
Solution Approach 1:
The patent performs preliminary sampling at multiple different sampling ratios to gather data about the power characteristics of the energy harvesting source. This preliminary action allows the system to build a knowledge base about optimal operating points before actual MPPT operation, enabling more accurate tracking without excessive complexity during normal operation.
Solution Approach 2:
The patent implements a feedback mechanism where the detected power information is used to adjust the sampling ratio for subsequent measurements. This closed-loop approach allows the system to learn from past measurements and continuously improve its power extraction efficiency, adapting to varying environmental conditions while maintaining manageable algorithm complexity.
3Measurement precision
If multiple sampling ratios are tested to find optimal MPP, then measurement precision improves, but the time required for power tracking increases
Solution Approach 1:
The patent applies partial action by selecting a limited set of predetermined sampling ratios rather than testing all possible ratios. This selective approach allows the system to achieve sufficient measurement precision without the time penalty of exhaustive searching. The system performs measurements at strategically chosen sampling points to efficiently locate the MPP.
Solution Approach 2:
The patent uses periodic sampling at multiple ratios to gather power information over time. By periodically measuring at different sampling ratios and analyzing the power trends, the system can accurately determine the MPP location while maintaining a reasonable tracking response time. The periodic nature of the measurements allows for efficient data collection and analysis.
Data Source
AI summary
Provided are a method and energy harvesting system for extracting maximum power from an input source. When the energy harvesting system includes a converter, a sensing part, an artificial intelligence (AI) calculator, and a resistor part, the method includes: sensing an input voltage and an input current from the input source using the sensing part, converting the input voltage and the input current corresponding to analog values into digital values through an analog-to-digital converter (ADC) block, and transmitting the digital values to the AI calculator; calculating input power on the basis of the digital values using the AI calculator and acquiring an optimal sampling ratio for extracting maximum power on the basis of the input power; controlling a resistor divider ratio on the basis of the optimal sampling ratio to equalize a sampling ratio to the optimal sampling ratio; and performing a regulation operation through the converter.