Utilizing Dynamic Programming to Aid in the Hybrid Electric Vehicle (HEV) Component Selection Process to Minimize the Vehicle’s Fuel Consumption
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Publisher:The Ohio State University
Series/Report no.:The Ohio State University. Department of Mechanical and Aerospace Engineering Honors Theses; 2019
The EcoCAR Mobility Challenge is a four-year competition sponsored by General Motors, Department of Energy, and MathWorks that challenges 12 universities to transform a conventional 2019 Blazer into a semi-autonomous connected and hybrid vehicle. During the first year of the competition, the team determines the vehicle architecture that meets three engineering goals: reducing fuel consumption, maintaining stock vehicle drive quality, and ensuring a minimum acceleration performance. To determine the appropriate vehicle architecture, a design search was performed that utilized various simulations platforms to narrow the design space to one solution. This research focuses on the usage of dynamic programming as a tool to properly size components with regards to increasing the vehicle’s fuel economy. For a multiple-stage decision making process, dynamic programming (DP) minimizes a cost function through backward calculation over a sequence of decisions. For this applied research, DP computes the optimal control variables associated with the hybrid torque split and transmission gear state at each specific time step of a drive cycle. Also, DP eliminates control variable combinations that cause components to operate in an infeasible way. The advantage of DP is the determination of the global minimum fuel consumption, which guarantees the component configuration are fairly evaluated against one another. This research goes over the process and results of sizing a hybrid’s energy storage system, front powertrain system, electric motor, and rear final drive ratios.
Academic Major: Mechanical Engineering
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