A method to study and enhance the energy efficiency of soft electrostatic actuators - Steven L. Zhang; Toshihiko Fukushima; Sophie Kirkman; Soo Jin Adrian Koh; Philipp Rothemund; Christoph Keplinger (Proceedings of the National Academy of Sciences, 2026)
Actuators drive robotic motion, and their energy conversion efficiency is a key performance metric that informs power consumption. Soft electrostatic actuators promise new opportunities for bioinspired and wearable robotics, being driven by electrical signals and producing high-speed, muscle-like motion. Unlike electromagnetic motors, for which efficiency has been systematically studied, efficiency of soft actuators lacks a standardized definition and measurement method, highlighting the need for a unified framework for the evaluation of their efficiency. Here, we propose a comprehensive method to study electrical-to-mechanical energy conversion in soft electrostatic actuators by analyzing closed cycles on planes spanned by work-conjugate variables: voltage–charge and force–position; our experimental setup allows us to prescribe and measure in real-time all work-conjugate variables and thus, to evaluate efficiency as function of load, electric potential, frequency, and actuator materials. We introduce a practical work cycle to evaluate actuators, and, using Peano-HASEL (Hydraulically Amplified Self-healing ELectrostatic) actuators as a model system, we reveal that efficiency is highly dependent on applied voltage, force, and actuation frequency; within the tested range of parameters, we measure a maximum efficiency of 63.6%, which is more than three times the previously reported value for HASEL actuators. We further study energy losses inherent in mechanical and electrical cycles. We show the general applicability of our method across different electrostatic actuators by applying it to a pure-shear dielectric elastomer actuator (DEA), demonstrating efficiencies up to 62.9%. This comprehensive method will facilitate the study and development of electrostatic actuators for the next generation of highly efficient soft robots.