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This paper presents a robust sliding-mode observer (RSMO) for state-of-charge (SOC) estimation of a lithium-polymer battery (LiPB) in electric vehicles (EVs). A radial basis function (RBF) neural network (NN) is employed to adaptively learn an upper bound of system uncertainty. The switching gain of the RSMO is adjusted based on the learned upper bound to achieve asymptotic error convergence of the SOC estimation. A battery equivalent circuit model (BECM) is constructed for battery modeling, and its BECM is identified in real time by using a forgetting-factor recursive least squares (FFRLS) algorithm. The experiments under the discharge current profiles based on EV driving cycles are conducted on the LiPB to validate the effectiveness and accuracy of the proposed framework for the SOC estimation. [ABSTRACT FROM AUTHOR]

In this paper, a distributed event-triggered algorithm was proposed to solve a convex quadratic optimization problem of multi-agent systems under undirected and connected topologies. The event-triggered condition of each agent just requires its own state value and the state values of its neighbors at the triggering time, and hence the continuous communication and calculation are not required. Moreover, the minimum event-triggered interval is bounded by the sampling time and the Zeno behavior is therefore naturally avoided. The result is also extended to the networks with undirected and switching topologies. Numerical simulations show the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]