A Novel SOC Estimation for Hybrid Energy Pack using Deep Learning

TL;DR

This paper introduces a deep learning approach using NARXNN to accurately estimate the state of charge in hybrid energy storage systems of electric vehicles, significantly improving precision and efficiency.

Contribution

It presents a novel NARX neural network-based method for SOC estimation in EVs with hybrid energy storage, addressing nonlinear behaviors more effectively than existing methods.

Findings

SOC estimation accuracy improved by 91.5%

Error values below 0.1% achieved

Consumption time reduced by 11.4%

Abstract

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.