video1.0<iframe src="https://www.loom.com/embed/93291af18bf344c69a9a546eca28857d" frameborder="0" width="1226" height="919" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>9191226Loomhttps://www.loom.com9191226https://cdn.loom.com/sessions/thumbnails/93291af18bf344c69a9a546eca28857d-0ae118c8814db082.gif185.905In this video, I introduced our project, Revamp, which addresses the management of second-life EV batteries. We built a Python simulation environment using the PyVAM library to monitor three EV batteries, providing vital statistics like state of charge, voltage, current, and operating temperature. Our dashboard, powered by a Raspberry Pi running QNX, also features anomaly tracking to showcase our system's capabilities. Additionally, we utilized the Gemini API for data visualizations and performance insights, including a battery health predictor to assess the long-term performance of retired batteries. I encourage you to explore the dashboard and provide feedback on its functionality.