Beta Source Power System with AI-Driven Storage Selection
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Solution Overview
Problem
Beta cell-based power systems provide low power efficiency and are limited by the need for semi-permanent solutions in extreme environments where battery replacement is difficult, due to their reliance on micro-power generation.
Innovation Solution
A beta source-based power system incorporating artificial intelligence, including a power generating section, storage, multiplexer, optical power learning section, optimal power selecting section, output devices, and de-multiplexer, which uses machine learning to estimate state of charge and select optimal power storage for efficient power distribution to IoT sensors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Duration of action of stationary object
If a beta cell is used to provide power in extreme environments, then the device can operate semi-permanently without external power sources, but the power efficiency is lower compared to chemical or physical cells
Solution Approach 1:
The patent divides the power system into multiple beta source-based generators, each contributing to the total power output. This segmentation allows the system to accumulate sufficient power while maintaining the semi-permanent operation characteristic, resolving the contradiction between extended battery life and adequate power efficiency.
Solution Approach 2:
The patent combines multiple beta source-based generators into a single power system, merging their individual micro-power outputs into a collective power source that achieves both extended operation duration and improved overall power efficiency, addressing the limitation of single beta cell power output.
2Power
If machine learning is implemented to optimize power selection, then power efficiency is improved, but the device complexity increases
Solution Approach 1:
The patent implements a self-service mechanism where the system automatically monitors the state of charge of multiple power storages and uses machine learning to autonomously select the optimal power source. This self-service approach improves power efficiency while minimizing the need for external control, thereby limiting the increase in device complexity.
Solution Approach 2:
The patent incorporates a feedback mechanism where the machine learning model continuously monitors power storage states and adjusts power selection based on learned patterns. This feedback loop enables the system to achieve high power efficiency through adaptive optimization while keeping the control architecture relatively simple and manageable.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system achieves optimal power efficiency and semi-permanent power supply without an external source, enhancing reliability by deriving optimal power through machine learning in a beta source-based system.
Implementation Method 1
a beta cell using beta rays emitted from a radioactive isotope has been proposed. The beta cell produces electrical energy by absorbing the beta ray emitted from a beta emitter, such as an isotope, into a PN junction layer of a semiconductor
Implementation Method 2
The beta cell produces electrical energy by absorbing the beta ray emitted from a beta emitter, such as an isotope, into a PN junction layer of a semiconductor
Data Source
AI summary
Provided herein are a power system based on a beta source and an operating method thereof. The system includes a power generating section including a plurality of beta source-based generators, a power storage section including a plurality of power storages to store electrical energy which is generated from the generators, a multiplexer configured to select at least some of the storages, an optical power learning section to receive electrical signals provided from the storages, and estimate a state of charge (SOC) of each of the storages, through machine learning, an optimal power selecting section to select a power storage, which provides the optimal power, based on the SOC of each of the storages, an output section including a plurality of output devices to output power provided from the storage selected by the optimal power selecting section, and a de-multiplexer to select at least one output device of the output devices.