A Set of Algorithmic Models of Product Platforming and Learning by Doing to Inform Battery Manufacturing Decisions and Processes

 The manufacturing cost and consumer demand for battery electric vehicles (BEVs) is critical to determining effective decisions for policymakers and vehicle manufacturers to reduce greenhouse gas (GHG) emissions. Increased globalization has provided opportunities for companies to reduce costs by creating common platforms across products sold in different countries. Cost reductions are also made possible through increasing production line efficiency through learning by doing, which grows as a company’s cumulative production increases. This dissertation develops models to analyze these sources of cost reductions and inform manufacturer and policy decisions. Chapter 1 provides an introduction that motivates the problem. In Chapter 2, the global platforming problem is modeled as a Nash equilibrium among oligopolistic competing firms, each maximizing its profit across markets with respect to its pricing, design, and platforming decisions. We develop and compare two methods to identify Nash equilibria: (1) a sequential iterative optimization (SIO) algorithm, in which each firm solves a mixed-integer nonlinear programming problem globally, with firms iterating until convergence; and (2) a mathematical program with equilibrium constraints (MPEC) that solves the Karush Kuhn Tucker conditions for all firms simultaneously. The algorithms’ performance and results are compared in a case study of plug-in hybrid electric vehicles where firms choose optimal battery capacity and whether to platform or differentiate battery capacity across the US and Chinese markets. Chapter 3 presents a learning curve model for electric vehicle batteries based on engineering process models of battery cell and pack production lines that is both consistent with potential sources of cost reductions in the production process and transparent with respect to assumptions of future material costs and battery chemistries.