A data-model fusion method for online state of power estimation of lithium-ion batteries at high discharge rate in electric vehicles

Abstract

This paper proposes a novel data-model fusion method (DMFM) for online state of power (SOP) estimation of lithium-ion batteries at high discharge rates in electric vehicles. First, battery polarisation characteristics responding for high discharge rates are experimentally investigated through a series of decremental pulse tests. Battery polarisation voltage is observed with diverse growing patterns over a whole battery operation range, and its underlying correlations with state of charge (SOC), discharge rate and pulse runtime are recognised. Second, a feed-forward neural network (FFNN) with SOC, discharge rate and pulse runtime as inputs, is constructed to characterise battery polarisation voltage through modelling the current excited polarisation resistance. Third, a DMFM is proposed to combine the data-driven method and equivalent-circuit model based method for accurate online SOP estimation in a lengthy prediction window ranging from 30 s to 120 s. Moreover, an unscented Kalman filter is devised to filter the estimation outcomes of the DMFM for noise suppression. The experimental results validate the effectiveness of the constructed FFNN in reproducing the nonlinearity of battery polarisation characteristics at high discharge rates and show the significant improvement in SOP estimation accuracy.