Optimizing the electric multirotor aerial vehicle performance through inertia-preserved velocity and SOE estimation

This paper introduces advanced frameworks to enhance the performance of electric multirotors for Urban Air Mobility (UAM) applications. Key contributions include the battery State-of-Energy (SOE) estimation model, which is based on aerodynamics and momentum theory. Additionally, a discrete-time state-space framework integrates vehicle dynamics with SOE, refined using an Extended Kalman Filter (EKF). Furthermore, an algorithm and a Model Predictive Control (MPC) method are introduced to enhance energy efficiency during horizontal forward flight trajectory (Cruise Phase). These approaches utilize inertia-preserved velocity to produce Impulse Horizontal Thrusts (IHT) rather than Continuous Horizontal Thrusts (CHT). Simulation results indicate approximately 26% energy savings achieved with these strategies, highlighting their potential to boost the efficiency and feasibility of UAM substantially.