A Strategy for State of Charge Estimation of Lithium-Ion Battery via an Adaptive Cubature Kalman Filter Based on Fractional-Order Model

Abstract

Accurate state of charge (SOC) estimation of lithium-ion battery (LIB) is beneficial for battery management systems (BMS) to optimize the management of LIB, which has significant implications for the operation of electric vehicles. In this article, SOC estimation of LIB is achieved via an adaptive cubature Kalman filter (ACKF) based on the fractional-order model (FOM), where FOM consists of one parallel resistance-constant phase pairs and one Warburg unit (FO-RCW). The parameters and orders in FO-RCW model are identified via multiswarm cooperative particle swarm optimizer (MCPSO) based on the experimental data. The sigmoid function is employed to address potential boundary exceedance issue of SOC and order. The parameters and orders of the LIB model are operation-dependent, varying under different working conditions, therefore, the augmented vector method is adopted, integrating the identified model parameters and orders (as initial value), and SOC into a augmented state vector. Subsequently, an ACKF based on the FO-RCW model is proposed to achieve SOC estimation and online parameter and order estimation. Finally, the effectiveness and superiority of ACKF based on FO-RCW model are validated via experiments.