  Estimating the state of charge (SOC) of compound energy storage devices in
the hybrid energy storage system (HESS) of electric vehicles (EVs) is vital in
improving the performance of the EV. The complex and variable charging and
discharging current of EVs makes an accurate SOC estimation a challenge. This
paper proposes a novel deep learning-based SOC estimation method for
lithium-ion battery-supercapacitor HESS EV based on the nonlinear
autoregressive with exogenous inputs neural network (NARXNN). The NARXNN is
utilized to capture and overcome the complex nonlinear behaviors of lithium-ion
batteries and supercapacitors in EVs. The results show that the proposed method
improved the SOC estimation accuracy by 91.5% on average with error values
below 0.1% and reduced consumption time by 11.4%. Hence validating both the
effectiveness and robustness of the proposed method.
