State of charge estimation for the lithium-ion battery based on fractional-order multi-dimensional Taylor network

Abstract

To accurately estimate the state of charge (SOC) of the lithium-ion battery (LiB), a fractional-order multi-dimensional Taylor network (FMTN) model was proposed in the study. With two open datasets of LiBs, the performance of the FMTN model on the SOC estimation is evaluated and compared with multi-dimensional Taylor network (MTN), back propagation neural network (BPNN), and dual-stage attention gate recurrent unit neural network (DA-GRUNN) models. With the datasets of B0005 and B0006 LiBs, the SOC estimation accuracy of the FMTN-6 model is 34 %, 32 %, and 37 %, 45 % higher than that of the MTN-6 and BPNN-3 models, respectively. The estimation time consumptions of the FMTN-6 model are increased by 2.8 % and 3.9 % and reduced by 49.5 % and 52.8 % compared with that of the MTN-6 and BPNN-3 models, respectively. With the open dataset of real driving conditions, the root-mean-square error of the SOC estimation of the FMTN model is reduced by 48 % compared with that of the MTN model. Besides, with the datasets at 25 °C and 45 °C, the SOC estimation accuracy of the FMTN model is improved by 17 % and 18 % compared with that of the DA-GRUNN model, respectively.