The focus of the current research is to investigate the potential of using bistable unsymmetric cross-ply laminated composites as a means for achieving structures with morphed characteristics. To this end, an investigation of the design space for laminated composites exhibiting bistable behavior is undertaken and the key parameters controlling their behavior are identified. For this purpose a nonlinear Finite Element methodology using ABAQUS code is developed to predict both the cured shapes and the stability characteristics of unsymmetric cross-ply laminates. In addition, an experimental program is developed to validate the analytically predicted results through comparison with test data. A new method is proposed for attaching piezoelectric actuators to a bistable panel in order to preserve its favorable stability characteristics as well as optimizing the actuators performance. The developed nonlinear FE methodology is extended to predict the actuation requirements of bistable panels. Actuator requirements, predicted using the nonlinear FE analysis, are found to be in agreement with the test results. The current research also explores the potential for implementing bistable panels for Uninhabited Aerial Vehicle (UAV) wing configuration. To this end, a set of bistable panels is manufactured by combining symmetric and unsymmetric balanced and unbalanced stacking sequence and their stability characteristics are predicted. A preliminary analysis of the aerodynamic characteristics of the manufactured panels is carried out and the aerodynamic benefits of manufactured bistable panel are noted.

Due to the large share of production costs, the lifespan of an electric vehicle’s (EV) lithium-ion traction battery should be as long as possible. The optimisation of the EV’s operating strategy with regard to battery life requires a regular evaluation of the battery’s state-of-health (SOH). Yet the SOH, the remaining battery capacity, cannot be measured directly through sensors but requires the elaborate conduction of special characterisation tests. Considering the limited number of test facilities as well as the rapidly growing number of EVs, time-efficient and scalable SOH estimation methods are urgently needed and are the object of investigation in this work. The developed virtual SOH experiment originates from the incremental capacity measurement and solely relies on the commonly logged battery management system (BMS) signals to train the digital battery twins. The first examined dataset with identical load profiles for new and aged battery state serves as proof of concept. The successful SOH estimation based on the second dataset that consists of varying load profiles with increased complexity constitutes a step towards the application on real driving cycles. Assuming that the load cycles contain pauses and start from the fully charged battery state, the SOH estimation succeeds either through a steady shift of the load sequences (variant one) with an average deviation of 0.36% or by random alignment of the dataset’s subsequences (variant two) with 1.04%. In contrast to continuous capacity tests, the presented framework does not impose restrictions to small currents. It is entirely independent of the prevailing and unknown ageing condition due to the application of battery models based on the novel encoder–decoder architecture and thus provides the cornerstone for a scalable and robust estimation of battery capacity on a pure data basis.