Model predictive emissions control of a diesel engine airpath: Design and experimental evaluation

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/163480/2/rnc5188.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/163480/1/rnc5188_am.pd

Energy and Emissions Conscious Optimal Following for Automated Vehicles with Diesel Powertrains

The emerging application of autonomous driving provides the benefit of eliminating the driver from the control loop, which offers opportunities for safety, energy saving and green house gas emissions reduction by adjusting the speed trajectory. The technological advances in sensing and computing make it realistic for the vehicle to obtain a preview information of its surrounding environment, and optimize its speed trajectory accordingly using predictive planning methods. Conventional speed following algorithms usually adopt an energy-centric perspective and improve fuel economy by means of reducing the power loss due to braking and operating the engine at its high fuel efficiency region. This could be a problem for diesel-powered vehicles, which rely on catalytic aftertreatment system to reduce overall emissions, as reduction efficiency drops significantly with a cold catalyst that would result from a smoother speed profile. In this work, control and constrained optimization techniques are deployed to understand the potential for and achieve concurrent reduction of fuel consumption and emissions. Trade-offs between fuel consumption and emissions are shown using results from a single objective optimal planning problem when the calculation is performed offline assuming full knowledge of the whole cycle. Results indicate a low aftertreatment temperature when energy-centric objectives are used, and this motivates the inclusion of temperature performance metric inside the optimization problem. An online optimal speed planner is then designed for concurrent treatment of energy and emissions, with a limited but accurate preview information. An objective function comprising an energy conscious term and an emissions conscious term is proposed based on its effectiveness of 1) concurrent reduction of fuel and emissions, 2) flexible balancing between the emphasis on fuel saving or emissions reduction based on performance requirements and 3) low computational complexity and ease of numerical treatment. Simulation results of the online optimal speed planner over multiple drive cycles are presented, and for the vehicle simulated in this work, concurrent reduction of fuel and emissions is demonstrated using a specific powertrain, when allowing flexible modification of the drive cycle. Hardware-in-the-loop experiment is also performed over the Federal Test Procedure (FTP) drive cycle, and shows up to 15% reduction in fuel consumption and 70% reduction in NOx emissions when allowing a flexible following distance. Finally, the stringent requirement of accurate preview information is relaxed by designing a robust re-formulation of the energy and emissions conscious speed planner. Improved fuel economy and emissions are shown while satisfying the constraints even in the presence of perturbations in the preview information. A Gaussian mixture regression-based speed prediction is applied to test the performance of the speed following strategy without assuming knowledge of the preview information. A performance degradation is observed in simulation results when using the predicted velocity compared with an accurate preview, but the speed planner preserves the capability to improve fuel and tailpipe emissions performance compared with a non-optimal controller.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/170004/1/huangchu_1.pd

Variational and Time-Distributed Methods for Real-time Model Predictive Control

This dissertation concerns the theoretical, algorithmic, and practical aspects of solving optimal control problems (OCPs) in real-time. The topic is motivated by Model Predictive Control (MPC), a powerful control technique for constrained, nonlinear systems that computes control actions by solving a parameterized OCP at each sampling instant. To successfully implement MPC, these parameterized OCPs need to be solved in real-time. This is a significant challenge for systems with fast dynamics and/or limited onboard computing power and is often the largest barrier to the deployment of MPC controllers. The contributions of this dissertation are as follows. First, I present a system theoretic analysis of Time-distributed Optimization (TDO) in Model Predictive Control. When implemented using TDO, an MPC controller distributed optimization iterates over time by maintaining a running solution estimate for the optimal control problem and updating it at each sampling instant. The resulting controller can be viewed as a dynamic compensator which is placed in closed-loop with the plant. The resulting coupled plant-optimizer system is analyzed using input-to-state stability concepts and sufficient conditions for stability and constraint satisfaction are derived. When applied to time distributed sequential quadratic programming, the framework significantly extends the existing theoretical analysis for the real-time iteration scheme. Numerical simulations are presented that demonstrate the effectiveness of the scheme. Second, I present the Proximally Stabilized Fischer-Burmeister (FBstab) algorithm for convex quadratic programming. FBstab is a novel algorithm that synergistically combines the proximal point algorithm with a primal-dual semismooth Newton-type method. FBstab is numerically robust, easy to warmstart, handles degenerate primal-dual solutions, detects infeasibility/unboundedness and requires only that the Hessian matrix be positive semidefinite. The chapter outlines the algorithm, provides convergence and convergence rate proofs, and reports some numerical results from model predictive control benchmarks and from the Maros-Meszaros test set. Overall, FBstab shown to be is competitive with state of the art methods and to be especially promising for model predictive control and other parameterized problems. Finally, I present an experimental application of some of the approaches from the first two chapters: Emissions oriented supervisory model predictive control (SMPC) of a diesel engine. The control objective is to reduce engine-out cumulative NOx and total hydrocarbon (THC) emissions. This is accomplished using an MPC controller which minimizes deviation from optimal setpoints, subject to combustion quality constraints, by coordinating the fuel input and the EGR rate target provided to an inner-loop airpath controller. The SMPC controller is implemented using TDO and a variant of FBstab which allows us to achieve sub-millisecond controller execution times. We experimentally demonstrate 10-15% cumulative emissions reductions over the Worldwide Harmonized Light Vehicles Test Cycle (WLTC) drivecycle.PHDAerospace EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/155167/1/dliaomcp_1.pd