Optimizing the Production rate of EV battery cell in an EPQ model with process-based cost method using Genetic Algorithm: A case study of NMC-622 cell

Abstract

This research is motivated by a real-world industry problem. Environmental concerns caused by the increasing number of internal combustion engine vehicles are developing a growing interest in the study and development of electric vehicle (EV) batteries. The cost of EV batteries is critical for the market growth of electric vehicles. As cell is the most essential component in the EV battery, the cost-effective manufacturing of battery cells is a popular topic in industry and academics. Manufacturers invest billions of dollars in battery cell factories based on predicted EV growth rates. However, these manufacturers require information on total manufacturing costs, plant area, total capital equipment costs, and their cost drivers to achieve the goal of a profitable firm. Inspired by these concerns, an EPQ model with a process-based cost modeling technique is developed for the large-scale manufacturing of EV battery cells. The goal of this research is to provide the most precise framework for the manufacturer to maximize its profit in cell manufacturing. The data used in this model is collected from the BatPac model (version 4) developed by Argonne National Laboratory. This study considers two types of battery cells used in electric vehicles, and the firm produces 5 % of defective cells. A well-known cost estimation method (PBCM) and the EPQ model are combined to generate total profit function. The production rate and selling price for both cells are considered as decision variables. The profit function is maximized by using genetic algorithm and graphs are provided to show the relation between decision variables and profit function. According to the findings, the production rate of the cells has a significant impact on the overall profit, and in order to maximize the profit, the production rate and selling price of cell 1 must be lower than cell 2. A detailed cost analysis has been provided to identify which process steps and cost aspects significantly impact the total cost. Finally, managerial implications and conclusions are presented that support manufacturers in increasing the firm’s profit.