Battery Level Estimation of Mobile Agents under Communication Constraints

We consider a team of mobile agents where a leader has to monitor battery levels of all other agents. Only the leader is capable to transmit information to other agents. Every now and then, the leader commands other agents to move toward or against the leader with speed proportional to battery level of the agent. The leader then simultaneously estimate the battery life of other agents from measurements of the relative distances between itself and other agents. We propose a nonlinear system model that integrates a particle motion model and a dynamic battery model that has demonstrated high accuracy in battery capacity prediction. The extended Kalman filter (EKF) is applied to this nonlinear model to estimate the battery level of each agent. One improvement we have made to the EKF is that in addition to gain optimization, the motion of agents can also be controlled to minimize estimation error. Simulation results are presented to demonstrate effective of the proposed method.

• Type: Journal
• Year: 2010
• Where: SUTC
• Authors: Jonghoek Kim, Fumin Zhang, Magnus Egerstedt
• Keywords: Battery Capacity Prediction, Battery Level, Dynamic Battery Model, Sensor Networks, SUTC 2010