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In this paper, a model fusion method (MFM) is proposed for online state of charge (SOC) and state of power (SOP) co-estimation of lithium-ion batteries (LIBs) in electric vehicles (EVs). Firstly, a particle swarm optimization-genetic algorithm (PSO-GA) method is cooperated with a 2-RCCPE fractional-order model (FOM) to construct battery open-circuit voltage (OCV)-SOC curve, which only relies on a part of dynamic load profile without the prior knowledge of an initial SOC. Secondly, a dual extended Kalman filter (DEKF) algorithm based on a 1-RC model is employed to identify the model parameters and estimate battery SOC with the extracted OCV-SOC curve. Furthermore, battery polarization dynamics in a SOP prediction window is analyzed from two aspects: (1) self-recovery; and (2) current excitation. They are separately simulated using 2-RCCPE FOM and 1-RC model, and then integrated through a model fusion for online SOP estimation, which enables an analytical expression of battery peak charge/discharge current in a prediction window without weakening the nonlinear characteristic of FOM. Experimental results demonstrate the improved performance of the proposed MFM for online discharge SOP estimation, where the mean absolute error and root mean square error are only 0.288 W and 0.35 W, respectively, under the urban dynamometer driving schedule profile. [ABSTRACT FROM AUTHOR]

Battery models are the cornerstone to battery state of charge (SOC) estimation and battery management systems in electric vehicles. This paper proposes a novel fractional-order model for a battery, which considers both Butler–Volmer equation and fractional calculus of constant phase element. The structure characteristics of the proposed model are then analyzed, and a novel identification method, which combines least squares and nonlinear optimization algorithm, is proposed. The method is proven to be efficient and accurate. Based on the proposed model, a fractional-order unscented Kalman filter is developed to estimate SOC, while singular value decomposition is applied to tackle the nonlinearity of Butler–Volmer equation and fractional calculus of constant phase element. The systematic comparison between the proposed model and traditional fractional order model is carried out on two LiNiMnCo lithium-ion batteries at different temperatures, ageing levels, and electric vehicle current profiles. The comparison results show that the proposed model has higher estimation accuracy in battery terminal voltage and SOC than the traditional model over wide range of temperature and ageing level under electric vehicle operation conditions. Furthermore, the hardware-in-the-loop test validates that the proposed SOC estimation method is suitable for SOC estimation in electric vehicles. [ABSTRACT FROM AUTHOR]

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]