Event-driven piezoelectric energy harvesting for railway field applications

Shuai Qu, Yuhao Ren, Guobiao Hu, Wei Ding, Liwei Dong, Jizhong Yang, Zaixin Wu, Shengyang Zhu, Yaowen Yang and Wanming Zhai

Applied Energy, 2024, vol. 364, issue C, No S0306261924005439

Abstract: Piezoelectric energy harvesters (PEHs) hold promises for revolutionizing railway structural health monitoring (SHM). However, the challenges of designing high-robust PEHs for handling complex and ever-shifting vibrations in practical railway scenarios remain formidable. Unlike conventional methods that align the fundamental frequency of the PEH with the dominant frequency of the vibration source, this study employs an event-driven enhancement method. This study commences by employing a high-fidelity dynamic model of the PEH and an advanced vehicle-track coupled dynamic model to evaluate energy harvesting efficiency across various railway scenarios. The vehicle-induced track vibrations are predicted across various train speeds, track structures, and track irregularities, considering 12 distinct vehicle-track conditions and totaling 3600 simulation datasets, each representing a specific event. We employ the particle swarm optimization (PSO) algorithm to identify optimal PEH designs for different events. To streamline this process, instead of offering one optimal solution for each event, we employ the K-means algorithm to cluster similar events. Subsequently, we choose the centroid design for each cluster as the representative solution for events within that cluster. This approach allows us to use a limited number of representative PEHs to effectively address most events across different clusters. Finally, we thoroughly examine and evaluate the dynamic responses of these representative designs to demonstrate their robust performance. This study explores PEH design optimization from a fresh perspective, bridging the gap between theoretical design and practical implementation in rail transportation systems.

Keywords: Energy harvesting; Piezoelectric; Vehicle-track coupled dynamics; Cluster analysis; Intelligent optimization (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1016/j.apenergy.2024.123160

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