Smart structures mimic biological systems by using thousands of sensors serving as a nervous system analog. One approach to give structures this sensing ability is to develop a multifunctional sensor network. Previous work has demonstrated stretchable sensor networks consisting of temperature sensors and impact detectors for monitoring external environments and interacting with other objects. The objective of this work is to develop distributed, robust and reliable strain gauges for obtaining the strain distribution of a designated region on the target structure. Here, we report a stretchable network that has 27 rosette strain gauges, 6 resistive temperature devices and 8 piezoelectric transducers symmetrically distributed over an area of 150 × 150 mm to map and quantify multiple physical stimuli with a spatial resolution of 2.5 × 2.5 mm. We performed computational modeling of the network stretching process to improve measurement accuracy and conducted experimental characterizations of the microfabricated strain gauges to verify their gauge factor and temperature coefficient. Collectively, the results represent a robust and reliable sensing system that is able to generate a distributed strain profile of a common structure. The reported strain gauge network may find a wide range of applications in morphing wings, smart buildings, autonomous cars and intelligent robots.

This work presents a novel scalable and field deployable framework for estimating battery state of charge (SoC) based on pitch-catch guided wave propagation using lowprofile built-in piezoelectric transducers. A preliminary study is performed through experiments using piezoelectric disc transducers mounted on commercial lithium-ion (Li-ion) pouch batteries. Similar experiments are carried out on Multifunctional Energy Storage (MES) Composites, which are an energy-storing structural material recently developed by the authors. In this work, special emphasis is given to the numerical validation of the variation in guided wave time of flight (ToF) due to the SoC-induced changes in electrode mechanical properties. The experimental results are accurately captured in the numerical simulation, which provides significant insight on the complex coupling between electrochemistry and mechanics. Moreover, a first-pass statistical analysis is conducted which demonstrates the efficacy of using guided wave data to obtain accurate prediction of battery SoC. The study also reveals a promising opportunity for exploiting the feature-rich multi-path nature of guided wave propagation to further improve SoC prediction accuracy.