MULTI-OBJECTIVE OPTIMIZATION MODEL OF BATTERY SWAPPING STATIONS TO MINIMIZE COST AND BATTERY DEGRADATION

Abstract

Battery Swapping Stations (BSS) offer rapid energy exchange for electric vehicles while functioning as flexible grid assets. This study develops a multi-objective optimization framework utilizing Model Predictive Control (MPC) with convex programming (CVXPY) to balance electricity procurement costs against battery health. We employ a Linearised Throughput Penalty, calibrated from the Wöhler curve at 80% Depth of Discharge (DOD), to serve as a convex proxy for electrochemical degradation. The system is controlled via a 24-hour receding horizon simulated over a 168-hour (one week) operational period to capture diurnal load variances. Cost sensitivity analysis reveals that a conservative degradation penalty (????= $0.05/kWh) creates a robust trade- off, achieving a net weekly revenue of $160.08 while limiting Equivalent Full Cycles (EFC) to Preserving levels. Furthermore, V2G regulatory analysis quantifies the impact of export restrictions: prohibiting grid back-feeding generates an opportunity cost of ~$295/week due to curtailed renewable generation. The CVXPY solver demonstrates varying performance based on horizon length, achieving 45 ms computation time for the 24-hour control loop, validating suitability for real-time deployment.