Synopsis
Electric and hybrid passenger vehicles improve efficiency by recovering braking energy and optimizing power distribution. This study compares the energy-saving potential of three optimization methods—mixed-integer linear programming (MILP), nonlinear programming (NLP), and dynamic programming (DP)—for a battery-electric vehicle on the Urban Dynamometer Driving Schedule (UDDS). Using a simplified EV model, results show NLP achieves the highest energy reduction (31.1 %), followed by DP (23.5 %) and MILP (18.2 %).
