HyPER Robot Power Architecture Segmentation
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Solution Overview
Problem
Current robotic and unmanned vehicle systems face limitations in energy source efficiency and scalability, with little research into standardizing power systems beyond optimizing converter designs and energy management.
Innovation Solution
The HyPER system architecture, comprising scalable current sink-source modules, Energy Source Adapters, a Master Power Management Controller, and HyPER Load Adapters, allows for flexible power source optimization and management, enabling efficient energy allocation based on load frequency demands, and accommodating various energy storage devices and loads.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Duration of action of moving object
If a hybrid power system with multiple energy sources is implemented, then the operating time and range are extended, but the device complexity increases
Solution Approach 1:
The power system is segmented into modular Energy Source Adapters (ESAs), each independently managing a specific energy source (battery, ultracapacitor, fuel cell). This segmentation allows the system to achieve extended operating time through multiple sources while keeping each module relatively simple and manageable, reducing overall system complexity.
Solution Approach 2:
The ESAs are designed as universal interfaces that can accommodate different types of energy sources through standardized connections and control protocols. This multi-functionality allows the same architectural framework to manage diverse energy sources without proportionally increasing complexity, as each ESA serves multiple potential energy source types.
2Use of energy by moving object
If frequency-based power allocation is implemented, then energy efficiency is optimized, but the control system complexity increases
Solution Approach 1:
The Master Power Management Controller implements frequency-based power allocation by continuously monitoring the operational frequency and power demands of robotic loads. This feedback mechanism allows the system to dynamically optimize energy efficiency by matching energy source output characteristics to load requirements, while the automated feedback loop manages control complexity rather than requiring complex manual coordination.
Data Source
AI summary
Advances in robot performance have been limited by a lack of advances in the mature field of battery technology. The focus of robotic power systems must expand from the use of single energy devices to the inclusion of multiple devices which can be optimized for a robotic platform. The challenge lies in the development of the hardware and control algorithms for a scalable power delivery architecture which satisfies the power and energy requirements of most unmanned ground vehicles. This invention is directed to an architecture which is easily scalable and facilitates the use of a wide variety of energy storage/generation devices, while focusing on the system control algorithm and its stability. The experimental results for an example system are presented demonstrating that the architecture functions properly when faced with real world robotic power demands.