Date of Graduation

2021

Document Type

Thesis

Degree Type

MS

College

Statler College of Engineering and Mineral Resources

Department

Mechanical and Aerospace Engineering

Committee Chair

Andrew Nix

Committee Member

Scott Wayne

Committee Member

Mario Perhinschi

Abstract

As regulations on the emission of greenhouse gasses continue to tighten on the automotive industry, the production of hybrid electric vehicles has gained significant popularity in recent years. With the increase in production, there has been a parallel demand in the advancement of both mechanical hardware and control system implementation used in these vehicles. A critical factor in the efficient operation of a hybrid electric vehicle is the energy management strategy where the goal is to maximize the efficient use of fuel energy to propel the vehicle. Designing a fuel-efficient control system is a complex challenge due to the degrees of freedom that exist in the control of a hybrid electric vehicle. Several methods exist for the real-time implementation of control strategies that employ heuristic or optimization-based algorithms; however, these control strategies typically rely on the results of offline optimization as a benchmark against which the control strategies are evaluated. Offline energy management optimization strategies require a pre-defined driving schedule for which the operation of the powertrain can be evaluated to determine the globally optimal control policy. The goal of this work is to develop a hybrid electric vehicle model that is suitable for use in a dynamic programming algorithm that provides the benchmark for optimal control of the hybrid powertrain. The benchmark analysis employs dynamic programming by backward induction to determine the globally optimal solution by solving the energy management problem starting at the final timestep and proceeding backwards in time. This method requires the development of a backwards facing model that propagates the wheel speed of the vehicle for the given drive cycle through the driveline components to determine the operating points of the powertrain. Although dynamic programming only searches the solution space within the feasible regions of operation, the benchmarking model must be solved for every admissible state at every timestep leading to strict requirements for runtime and memory. The backward facing model employs the quasi-static assumption of powertrain operation to reduce the fidelity of the model to accommodate these requirements. Verification and validation testing of the dynamic programming algorithm is conducted to ensure successful operation of the algorithm and to assess the validity of the determined control policy against a high-fidelity forward-facing vehicle model with a percent difference of fuel consumption of 1.2%. The benchmark analysis is conducted over multiple drive cycles to determine the optimal control policy that provides a benchmark for real-time algorithm development and determine control trends that can be used to improve existing algorithms. The optimal combined CS fuel economy of the vehicle is determined by the dynamic programming algorithm to be 32.99 MPG, a 52.6% increase over the stock 3.6L 2019 Chevrolet Blazer.

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