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Distributed Supervisory Controller Design for Battery Swapping Modularity in Plug-in Hybrid Electric Vehicles.

By Shifang Li

Abstract

As industry strives to standardize engineering design, manufacturing, and maintenance processes, the focus on achieving component modularity is increasing. Component swapping modularity (CSM) in control systems allows component change without redesign of the system level controller, while achieving the required system performance. Opportunities to achieve CSM are emerging in control systems consisting of smart components connected by bidirectional communication networks. By distributing a part of the controller into the component module, controller recalibration can be limited to only the component module when the component changes. In this dissertation, a novel Direct Method is proposed to generate the distributed controller with CSM through a bi-level optimization. The distributed controller enables CSM and provides required system performance for each component variant. The Direct Method is applied to throttle actuator CSM design in engine idle speed control. The results demonstrate that the new Direct Method improves the CSM results compared to the previous 3-Step Method. In addition, the Direct Method permits the designer to trade off desired system performance versus achievable CSM. The Direct Method is then applied to design a distributed supervisory controller for battery CSM in plug-in hybrid electric vehicles. A novel feedback based controller for the charge sustaining mode is proposed. For effective controller distribution, a method based on sensitivity analysis of the control signals with respect to the battery hardware parameter is introduced. The bi-level optimization problem for the distributed controller gains is solved using the Augmented Lagrangian Decomposition method. The results demonstrate that battery CSM can be achieved without compromising fuel economy compared to the centralized control case

Topics: Component Swapping Modularity, Distributed Control, Networked Control Systems, Plug-in Hybrid Electric Vehicle
OAI identifier: oai:deepblue.lib.umich.edu:2027.42/86264
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