The field of wearable hydraulics for human-assistive devices is expanding. One of the major challenges facing development of these systems is creating lightweight, portable power units. This project’s goal was to develop design strategies and guidelines with the use of analytical modeling to minimize the weight of portable hydraulic power supplies in the range of 50–300 W. Steady-state, analytical models were developed and validated for a system containing a lithium-polymer battery, brushless DC motor, and axial-piston pump. Component parameters such as motor size, pump size, and swashplate angle were varied to explore and develop four main design guidelines that can be used by designers to minimize overall system weight. First, it is often not beneficial to select the smallest sized electric motor that can provide the required torque and speed. Second, cooling systems generally do not help reduce overall system weight. Third, the gearbox between the electric motor and pump should be eliminated to reduce system weight. Fourth, iterative modeling is necessary to determine the various range of particular component parameters necessary to result in a minimal-weight system. The analytical model developed takes inputs of desired flowrate, pressure, and runtime, and outputs the combination of pump size, swashplate angle, and motor size that results in a minimal-weight system. The four design principles and the computer simulation are tools that can be used to either design a fully custom, weight-optimized power supply or to aid in the selection of commercially available components for a low-weight power supply.
- Dynamic Systems and Control Division
Optimization and Design Principles of a Minimal-Weight, Wearable Hydraulic Power Supply
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Nath, JD, & Durfee, WK. "Optimization and Design Principles of a Minimal-Weight, Wearable Hydraulic Power Supply." Proceedings of the ASME 2017 Dynamic Systems and Control Conference. Volume 1: Aerospace Applications; Advances in Control Design Methods; Bio Engineering Applications; Advances in Non-Linear Control; Adaptive and Intelligent Systems Control; Advances in Wind Energy Systems; Advances in Robotics; Assistive and Rehabilitation Robotics; Biomedical and Neural Systems Modeling, Diagnostics, and Control; Bio-Mechatronics and Physical Human Robot; Advanced Driver Assistance Systems and Autonomous Vehicles; Automotive Systems. Tysons, Virginia, USA. October 11–13, 2017. V001T30A002. ASME. https://doi.org/10.1115/DSCC2017-5046
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