Wind-Induced Vibration Energy Harvesting Using Piezoelectric Transducers Coupled with Dynamic Magnification

Flexible cylindrical structures subjected to wind loading experience vibrations from periodic shedding of vortices in their wake. Vibrations become excessive when the natural frequencies of the cylinder coincide with the vortex shedding frequency. In this study, cylinder vibrations are transmitted to a beam inside the structure via dynamic magnifier system. This system amplifies the strain experienced by piezoelectric patches bonded to the beam to maximize the conversion from vibrational energy into electrical energy. Realworld applicability is tested using a wind tunnel to create vortex shedding and comparing the results to finite element modeling that shows the structural vibrational modes. A crucial part of this study is conditioning and storing the harvested energy, focusing on theoretical modeling, design parameter optimization, and experimental validation. The developed system is helpful in designing wind-induced energy harvesters to meet the necessity for novel energy resources.

Energy Management of a Battery-Ultracapacitor Hybrid Energy Storage System in Electric Vehicles

Electric vehicle (EV) batteries tend to have accelerated degradation due to high peak power and harsh charging/discharging cycles during acceleration and deceleration periods, particularly in urban driving conditions. An oversized energy storage system (ESS) can meet the high power demands; however, it suffers from increased size, volume and cost. In order to reduce the overall ESS size and extend battery cycle life, a battery-ultracapacitor (UC) hybrid energy storage system (HESS) has been considered as an alternative solution. In this work, we investigate the optimized configuration, design, and energy management of a battery-UC HESS. One of the major challenges in a HESS is to design an energy management controller for real-time implementation that can yield good power split performance. We present the methodologies and solutions to this problem in a battery-UC HESS with a DC-DC converter interfacing with the UC and the battery. In particular, a multi-objective optimization problem is formulated to optimize the power split in order to prolong the battery lifetime and to reduce the HESS power losses. This optimization problem is numerically solved for standard drive cycle datasets using Dynamic Programming (DP). Trained using the DP optimal results, an effective real-time implementation of the optimal power split is realized based on Neural Network (NN). This proposed online energy management controller is applied to a midsize EV model with a 360V/34kWh battery pack and a 270V/203Wh UC pack. The proposed online energy management controller effectively splits the load demand with high power efficiency and also effectively reduces the battery peak current. More importantly, a 38V-385Wh battery and a 16V-2.06Wh UC HESS hardware prototype and a real-time experiment platform has been developed. The real-time experiment results have successfully validated the real-time implementation feasibility and effectiveness of the real-time controller design for the battery-UC HESS. A battery State-of-Health (SoH) estimation model is developed as a performance metric to evaluate the battery cycle life extension effect. It is estimated that the proposed online energy management controller can extend the battery cycle life by over 60%.