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Open Access Publications from the University of California

Turbocharged Engine Control for Fuel Efficiency and Torque Responsiveness

  • Author(s): Tan, Raechel Chu-Hui
  • Advisor(s): Tomizuka, Masayoshi
  • et al.
Abstract

Fuel economy standards for cars and other vehicles are growing increasingly stringent, thus motivating automakers to find ways to improve fuel efficiency. One popular strategy is to turbocharge a downsized (smaller displacement) engine, which can be more fuel efficient than a naturally aspirated engine delivering the same power output. However, turbocharged engines can be sluggish to respond to torque requests, which drivers often find undesirable. Unfortunately, improving torque responsiveness results in reduced fuel efficiency, and vice versa.

This dissertation explores two model-based control strategies to manage this tradeoff. The first strategy is a decentralized controller, in which the throttle and wastegate are controlled in separate loops. The throttle loop uses feedback linearization with supplemental PI control to obtain good torque tracking. The wastegate is opened or closed, based on a preview of the reference torque, to switch between fuel-optimal and torque-optimal modes. The second strategy is a multi-objective optimization scheme to obtain good fuel efficiency and fast torque response by controlling the throttle and wastegate simultaneously. Simulation results show promising performance from both strategies.

Additionally, the models used in these control methods are described in detail. A high-fidelity engine simulator in Simscape is used for controller validation. This simulator is too complex for controller design, so a simpler 4-state model is constructed. This model works well in continuous time, but the optimization-based control method requires a discrete-time model. Unfortunately, discretizing the 4-state model results in chattering due to numerical stiffness. This numerical stiffness is analyzed, and a solution is proposed to represent the throttle pressure ratio as a static map. This results in a 3-state model that is easily discretized.

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