Exploring the potential of onboard energy scavenging subsystems for generating valuable data

Abstract

We propose implementing an onboard energy scavenging subsystem utilizing piezoelectric materials, which serves the dual purpose of generating electrical energy and facilitating data acquisition for multifaceted applications. In a practical demonstration, we have engineered a fully functional prototype adept at gathering data via a piezoelectric-centric energy scavenging mechanism. This gathered data is seamlessly synchronized with GPS coordinates and timestamps, meticulously organized within a system architecture, and harnessed through meticulously crafted Python code. The wealth of data that we obtain from an onboard energy scavenging subsystem holds significant potential. It empowers us to discern road irregularities and potholes through intricate analytical methodologies while also facilitating a thorough assessment of asphalt quality. Furthermore, it enables real-time surveillance of vehicular suspension system health and offers a nuanced exploration of driver behavior patterns. In a pragmatic pursuit of actionable insights, the amassed data can be expeditiously conveyed to relevant authorities. These authorities can perform even deeper Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) to proactively initiate timely corrective measures, thereby elevating road safety standards and ensuring the maintenance of critical infrastructure. The Results and Discussion section underscores the attainment of substantial and noteworthy outcomes, further affirming the significance of our findings.