An optimization framework for minimizing the production cost of lithium-ion batteries in electrified vehicles

It has been recognized that the high cost of lithium-ion batteries (LIB) is one of the major obstacles to the commercialization of electrified vehicles (EVs). There is an ongoing effort to reduce the cost of LIBs both at the cell and pack level. Our goal in this study is to develop an optimization framework that can be used to determine best cell and pack design that would minimize pack production cost under predefined performance requirements. For this purpose, we first build a process-based cost model (PBCM) using Argonne National Laboratory's battery performance and cost model (BatPaC 5.0). PBCM allows us to model the production cost of the LIB by defining all the production steps and their process assumptions for a battery manufacturing plant. We then define the optimization problem by identifying decision variables from cell-to-pack level design parameters that have the most substantial effects on the battery cost and performance. We formulate and solve the optimization problem in MATLAB. Using our framework and data from BatPaC we perform cost optimization for four different vehicle types (PHEVs and BEVs) and four different cathode chemistries. We also conducted two different case studies to study the effects of manufacturing limitations on battery pack cost.