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Estimating the state of charge (SOC) of compound energy storage devices inthe hybrid energy storage system (HESS) of electric vehicles (EVs) is vital inimproving the performance of the EV. The complex and variable charging anddischarging current of EVs makes an accurate SOC estimation a challenge. Thispaper proposes a novel deep learning-based SOC estimation method forlithium-ion battery-supercapacitor HESS EV based on the nonlinearautoregressive with exogenous inputs neural network (NARXNN). The NARXNN isutilized to capture and overcome the complex nonlinear behaviors of lithium-ionbatteries and supercapacitors in EVs. The results show that the proposed methodimproved the SOC estimation accuracy by 91.5% on average with error valuesbelow 0.1% and reduced consumption time by 11.4%. Hence validating both theeffectiveness and robustness of the proposed method.

A Novel SOC Estimation for Hybrid Energy Pack using Deep Learning