Distributed unknown input and state estimation for nonlinear multi-agent systems with applications to battery management

Abstract

This paper proposes a novel filtering algorithm for simultaneous estimation of states and unknown inputs of a class of nonlinear discrete-time heterogeneous multi-agent systems. Based on the Taylor approximation of the nonlinear multiagent system, a distributed semi-cooperative switch-mode filter is developed to get the minimum-variance unbiased (MVU) estimation of the unknown inputs and states. Compared with the conventional decentralized EKF-based unknown input filter, the proposed distributed filter has a more relaxed existence condition, which makes it more applicable in reality. This new type of filter is then successfully applied to the simultaneous estimation of state of charge (SOC) and temperature of a battery pack for battery management of electric vehicles and grid-tied energy storage systems.