This work proposes and analyzes a structurally-integrated lithium-ion battery concept. The multifunctional energy storage composite (MESC) structures developed here encapsulate lithium-ion battery materials inside high-strength carbon-fiber composites and use interlocking polymer rivets to stabilize the electrode layer stack mechanically. These rivets enable load transfer between battery layers, allowing them to store electrical energy while also contributing to the structural load carrying performance, without any modifications to the battery chemistry. The design rationale, fabrication processes, and experimental mechano-electrical characterization of first-generation MESCs are discussed. Experimental results indicate that the MESCs offer electrochemical performance comparable to standard lithium-ion cells, despite the disruptive design change. The mechanical performance of MESCs is assessed via quasi-static three-point bending tests, with results showing significantly improved mechanical stiffness and strength over traditional pouch cells. The rivets minimize interlayer shear movement of the electrode stack, thus allowing it to maintain electrochemical functionalities while carrying mechanical bending. While minimal load application can cause permanent deformation of pouch cells, MESCs maintain their structural integrity and energy-storage capabilities under realistic repeated loading. The results obtained demonstrate the mechanical robustness of MESCs, which allow them to be fabricated as energy-storing structures for electric vehicles and other applications.

In this work, a novel data-based stochastic global identification framework is introduced for air vehicles operating under varying flight states and uncertainty. In this context, the term global refers to the identification of a model that is capable of representing the system dynamics under any admissible flight state based on data recorded from sample states. The proposed framework is based on stochastic time-series models for representing the system dynamics and aeroelastic response under multiple flight states, with each state characterized by several variables, such as the airspeed and angle of attack, forming a flight state vector. The method's cornerstone lies in the new class of Vector-dependent Functionally Pooled (VFP) models which allow the explicit analytical inclusion of the flight state vector into the model parameters and, hence, system dynamics. The experimental evaluation is based on a prototype bio-inspired self-sensing composite wing that is subjected to a series of wind tunnel experiments. Distributed micro-sensors in the form of stretchable sensor networks are embedded in the composite layup of the wing to provide the sensing capabilities. Data collected from piezoelectric sensors are employed for the identification of a stochastic global VFP model. The estimated VFP model parameters constitute two-dimensional functions of the flight state vector defined by the airspeed and angle of attack. The identified model is able to successfully represent the aeroelastic response of the wing under the admissible flight states via a minimum number of estimated parameters compared to standard identification approaches. The obtained results demonstrate the high accuracy and effectiveness of the proposed global identification framework, thus constituting a first step towards the next generation of fly-by-feel aerospace vehicles with state awareness capabilities.

This work presents a novel scalable and field-deployable framework for monitoring lithium-ion (Li-ion) battery state of charge (SoC) and state of health (SoH), based on ultrasonic guided waves using low-profile built-in piezoelectric transducers. The feasibility of this technique is demonstrated through experiments using surface-mounted piezoelectric disc transducers on commercial Li-ion pouch batteries. Pitch-catch guided-wave propagation is performed in synchronization with electrical charge and discharge cycling, and cycle life testing. Simple time-domain analysis shows strong and repeatable correlation between waveform signal parameters, and battery SoC and SoH. The correlation thus provides a building block for constructing a technique for accurate real-time monitoring of battery charge and health states using ultrasonic guided-wave signals. Moreover, capacity-differential signal analysis reveals the underlying physical changes associated with cyclic electrochemical activities and phase transitioning. This finding allows accurate pinpointing of the root cause of capacity fade and mechanical degradation. The results of this study indicate that the use of guided waves can potentially offer a new avenue for in-situ characterization of Li-ion batteries, providing insight on the complex coupling between electrochemistry and mechanics, heretofore not fully understood within the scientific community.

In this work, an experimental study of a novel data-driven fly-by-feel state awareness method is presented. A non-parametric probabilistic approach for stall detection is investigated and assessed via a series of wind tunnel experiments. The method is based on the statistical analysis of the recorded signals and subsequent statistical hypothesis testing and decision making procedures. In this proof-of-concept experimental study, the flight state is defined by two variables, the airspeed and angle of attack. The experimental evaluation and assessment is based on a prototype bio-inspired self-sensing composite wing that is subjected to a series of wind tunnel experiments under multiple flight states. Distributed micro-sensors, in the form of stretchable sensor networks, are embedded in the composite layup of the wing in order to provide the sensing capabilities. Experimental data collected from piezoelectric sensors are employed for the development and assessment of a non-parametric fly-by-feel stall detection approach within a probabilistic framework. In this study, special emphasis is given to the early detection of aerodynamic stall without making use of any conventional information related to the attitude of the vehicle. The method is able to provide in real time the probability of stall for an indicative flight scenario that was implemented in the wind tunnel. The obtained results demonstrate the effectiveness and potential of the developed approach

We propose the concept of Multifunctional-Energy-Storage Composites (MES Composites) which highlights a unique integration technique for embedding lithium-ion battery materials in structural carbon-fiber-reinforced-polymers (CFRP). Unlike standard lithium-ion pouch cells, the MES Composites maximizes material utilization by using CFRP facesheets to house the electrochemistry. Through-thickness polymer reinforcements are implemented to allow load transfer between the two facesheets, analogous to the sandwich structure construction.In this work, the design rationale, materials and fabrication techniques, experimental evaluation, and performance of the first-generation MES Composites will be presented. MES Composite cells with a nominal capacity of approximately 4 Ah, with various reinforcements-array configurations, were fabricated and first tested through a series of electrochemical reference performance tests (RPT) under a strain-free condition. The MES Composite cells then underwent a mechanical electrical-coupling test, where a quasi-static three-point-bending load was applied at increasing increments. Mechanical testing was interrupted after each increment to perform a sequential RPT to quantify any non-catastrophic degradation in the electrochemical performance.The obtained results verify the feasibility of the concept showing that the electrochemical performance of the MES Composites can be maintained at the same level as the regular lithium-ion battery. The reinforcement architecture of the MESComposite constrains the relative motion of the battery electrodes and increases the bending rigidity, resulting in a higher load carrying capacity and inhibiting non-fatal injury of the cell under mechanical loads. This multifunctional material system can also be scaled up and ultimately provide considerable weight and volume saving at the system level.

This work presents the development of the first-generation Multifunctional Energy Storage (MES) Composites a multifunctional structural battery which embeds li-ion battery materials into high-strength composites together with in-situ networks of sensors and actuators. MES Composites not only can supply electrical power but also serve as structural elements, capable of concurrently carrying mechanical loads. In addition, the built-in sensor/actuator networks can monitor the state and health of both of the composite structure as well as the battery on a real-time on-demand basis. As part of this study, MES Composite batteries are fabricated and then undergo a series of characterization tests to evaluate the synergistic energy-storage and load-carrying functionalities. Data generated from built-in sensors are also used to characterize the battery state of charge and health, in comparison with results from the electrochemical reference performance tests, and quantify any non-catastrophic degradation in the electrochemical performance. The obtained results verify the multifunctional capabilities of MES Composites for developing a novel minimum-weight and efficient energy storage system.

In this work, the system design, integration, and wind tunnel experimental evaluation are presented for a bio-inspired self-sensing intelligent composite unmanned aerial vehicle (UAV) wing. A total of 148 micro-sensors, including piezoelectric, strain, and temperature sensors, in the form of stretchable sensor networks are embedded in the layup of a composite wing in order to enable its self-sensing capabilities. Novel stochastic system identification techniques based on time series models and statistical parameter estimation are employed in order to accurately interpret the sensing data and extract real-time information on the coupled air flow-structural dynamics. Special emphasis is given to the wind tunnel experimental assessment under various flight conditions defined by multiple airspeeds and angles of attack. A novel modeling approach based on the recently introduced Vector-dependent Functionally Pooled (VFP) model structure is employed for the stochastic identification of the "global" coupled airflow-structural dynamics of the wing and their correlation with dynamic utter and stall. The obtained results demonstrate the successful system-level integration and effectiveness of the stochastic identification approach, thus opening new perspectives for the state sensing and awareness capabilities of the next generation of "fly-by-fee" UAVs.

Self-sensing intelligent composite materials with state-sensing and awareness capabilities constitute the future of aerospace structures. The objective of this work is to develop technologies that will lead to the next generation of intelligent aerospace structures that can sense the environmental conditions and structural state, effectively interpret the sensing data to achieve real-time state awareness, and employ appropriate self-diagnostics under varying operational environments. In this paper, the design, integration, and experimental identification of the structural dynamics and aeroelastic properties are presented for an intelligent composite UAV wing. Bio-inspired stretchable sensor networks, including integrated piezoelectric, strain, and temperature sensors are monolithically embedded in the composite layup to provide the sensing capabilities. Stochastic signal processing and identification techniques are employed in order to accurately interpret the sensing. The experimental evaluation and assessment is demonstrated via a series of wind tunnel experiment under varying angles of attack and airflow velocities for the identification of the coupled airflow-structural dynamics and strain distribution. The obtained results demonstrate the successful integration of the micro-fabricated stretchable sensor networks with the composite wing, as well as the effectiveness of the stochastic data interpretation approaches. This study constitutes a significant step in proving the integration potential of the approach for the next generation of fly-by-feel UAVs.

Self-sensing intelligent composite materials with state-sensing and awareness capabilities constitute the future of aerospace structures. The objective of this work is to develop technologies that will lead to the next generation of intelligent aerospace structures that can sense the environmental conditions and structural state, effectively interpret the sensing data to achieve real-time state awareness, and implement appropriate self-diagnostics under uncertainties in varying operational environments. In this paper, the design, integration, and experimental assessment are presented for a composite unmanned aerial vehicle (UAV) wing. Bio-inspired stretchable sensor networks, including integrated piezoelectric, strain, and temperature sensors are designed and monolithically embedded in the layup of the composite wing in order to provide the sensing capabilities. Stochastic signal processing and identification techniques are employed in order to accurately interpret the sensing data and assess the actual structural state. The experimental evaluation and assessment of the intelligent composite wing is demonstrated via wind tunnel experiment under varying angles of attack and airflow velocities for the identification of the coupled airflow-structural dynamics and investigation of the wing strain distribution for the considered conditions. The obtained results demonstrate the successful integration of the micro-fabricated stretchable sensor networks with the composite material of the wing, as well as the effectiveness of the stochastic data interpretation approaches proving their integration potential into fly-by-feel UAVs and enabling their deployment for aerospace applications.

SHM-based structures with embedded sensors and hardware have posed a great demand in distributed, in-situ power sources. This paper introduces the novel Multifunctional-Energy-Storage Composites (MES Composites) which highlights a unique integration technique for embedding lithium-ion battery materials in structural carbon-fiber-reinforced-polymers (CFRP). Unlike standard lithium-ion pouch cells, the MES Composites maximizes material utilization by using CFRP facesheets to house the electrochemistry. Through-thickness polymer reinforcements are implemented to allow load transfer between the two facesheets, analogous to the sandwich structure construction. A feasibility study and preliminary characterization have been completed and have shown that the same electrochemical performance as a standard Li-ion cell could be maintained, while achieving high bending rigidity.

Significant progress has recently been achieved in structural health monitoring, maturing the technology through quantification, validation, and verification to promote implementation and fielding of SHM. In addition, there is ongoing work seeking to detect damage precursors and to deploy structural health monitoring systems over large areas, moving the technology beyond hot-spot monitoring to global state sensing for full structural coverage. A large number of small sensors of multiple types are necessary in order to accomplish the goals of structural health monitoring, enabling increased sensing capabilities while reducing parasitic effects on host structures. Conventional sensors are large and heavy, adding to the weight of a structure and requiring physical accommodation without adding to and potentially degrading the strength of the overall structure. Increased numbers of sensors must also be deployed to span large areas while maintaining or increasing sensing resolution and capabilities. Traditionally, these sensors are assembled, wired, and installed individually, by hand, making mass deployment prohibitively time consuming and expensive. In order to overcome these limitations, the Structures and Composites Lab at Stanford University has worked to develop bio-inspired microfabricated stretchable sensor networks. Adopting the techniques of complementary metal-oxide semiconductor and microelectromechanical system fabrication, new methods are being developed to create integrated networks of large numbers of various micro-scale sensors, processors, switches, and all wiring in a single fabrication process. Then the networks are stretched to span areas orders of magnitude larger than the original fabrication area and deployed onto host structures. The small-scale components enable interlaminar installation in laminar composites or adhesive layers of built-up structures while simultaneously minimizing parasitic effects on the host structure. Additionally, data processing and interpretation capabilities could be embedded into the network before material integration to make the material truly multifunctional and intelligent once fully deployed. This article reviews the current accomplishments and future vision for these systems in the pursuit of state sensing and intelligent materials for self-diagnostics and health monitoring.