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- All Subjects: Control Optimization
- Creators: Zhao, Junfeng
Description
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.
ContributorsWild, Trevor (Author) / Chen, Yan (Thesis director) / Zhao, Junfeng (Committee member) / Barrett, The Honors College (Contributor) / Engineering Programs (Contributor)
Created2024-05