Model Predictive Control (MPC) is a fairly recent development in control optimization theory with high potential for use in the automotive industry, specifically in electric vehicle energy management systems. Because model predictive control is a particularly young concept and due to the MPC’s high computational load, it is overlooked when compared to conventional control methods such as Proportional Integral Derivative (PID) controllers. Among recent advancements in computing technology in electric vehicles, model predictive controllers have become a viable solution in electric vehicle (EV) Energy Management Systems (EMS). The distinction between MPCs and other EMS control methods can be summarized by MPC’s ability to optimize outputs in systems where multiple constraints and state-space variables are introduced where conventional methods cannot. The MPC achieves this by using predictive modeling, allowing it system states based on information provided through a feedback loop. Feasibility for the use of MPCs in EV EMSs will be supported by using a simulated dual-motor electric vehicle in SIMULINKs Virtual Vehicle Composer (VVC) application. Findings from repeated simulations have proven model predictive control to be an effective alternative optimization strategy for electric vehicle energy management systems.

The mission of EZ-Sit is to alleviate pain and increase comfort by creating a product that allows users to put their feet up when sitting for long periods of time. This product will connect to any single-stem office chair with ease and will provide users with the ability to put their feet up whenever and wherever they work. Our goal is to empower individuals to pursue their goals without the hindrance of discomfort that comes with sitting for extended periods of time. At EZ-Sit we believe that no one’s productivity should be impeded due to the pain caused by a sedentary work life. We hope that this product will bring about physical well-being in the workplace so individuals can focus on the day ahead of them.

SnackHax is a Founder's Lab venture that specializes in creating customized snack packages tailored to the needs of college students. Our packages include a variety of high-quality snacks designed to boost energy, support cognitive function, and promote overall wellness. With a focus on quality, affordability, and convenience, SnackHax aims to become the go-to source for students seeking nutritious and convenient snack packages. SnackHax is also looking to expand its product offerings to increase customer acquisition as well as expanding into other markets. Our thesis comprised of conducting market research and customer development to determine SnackHax's market fit and target customer. This was done through several tabling sessions on the Polytechnic and Tempe campuses where the team collected valuable data through the form of surveys, detailed observations, and Minimum Viable Products (MVPs). During the second semester, SnackHax finalized the products and began to offer them at the tabling events starting in the middle of February. Additionally, SnackHax made it into the New Venture Challenge course, led by Scott Wald, which helped further solidify the business and gain additional traction in the form of interest from investors. SnackHax finished the semester with 23 sales and a total revenue of $311.78.

Due to monumental advancements in large language models (LLMs), such as OpenAI's ChatGPT, there is widespread interest in integrating this general AI’s capabilities into various applications, including robotics. However, the rush to deploy this technology has left safety as an afterthought, if at all. This study investigates the potential for LLM-fused robots to operate safely in real-world settings. This study begins with a review of ChatGPT, highlighting its capabilities and current challenges, particularly with integrating LLMs into robotics, and continues with similar applications as AI agents though APIs. To assess the safety implications of LLM-driven robots, the study presents experimental methods involving the navigation of a TurtleSim robot in 2D environments when given different scenarios. Various parameters are analyzed to determine the current capabilities of ChatGPT to understand how to adjust any agents it possesses based on the situation. Current findings reveal that ChatGPT-driven robots demonstrate adaptive behavior based on the scenario provided, indicating their potential for real-time safety adjustments and eliciting further research to ensure safe and successful integration of these robots into diverse work environments.

Mechanical design can be intimidating, especially for someone new to the discipline because of the complexity and the limited number of resources available. The overarching goal of this project is to help mechanically curious individuals by creating an open-source 3D printed clock with detailed information and explanations for how the systems work and are designed. This increase in available knowledge will allow people to educate themselves by following or recreating the design process and, perhaps, inspire others to continue the learning process and study STEM.