Autonomous Vehicle Battery State-of-Charge Prognostics Enhanced Mission Planning

Abstract

Most mission planning algorithms are designed for healthy systems. When faults occur in a system, it is advantageous to optimize the mission plan by taking the system health condition into consideration. In this paper, a mission planning scheme is proposed to integrate real-time prognostics in a receding horizon path planning framework to accommodate the system fault. In this scheme, the state-of-charge of a battery is monitored and predicted by a particle-filtering based prognostic algorithm. The predicted state-of-charge and remaining useful life of the battery are used in the mission planning to minimize mission failure risk. A series of experiments are presented on a robotic platform, which is powered by a Lithium-ion battery, to demonstrate and verify the proposed scheme.