A Novel SOC Estimation for Hybrid Energy Pack using Deep Learning
This addresses the problem of SOC estimation for electric vehicle manufacturers and engineers, representing an incremental improvement using a hybrid method.
The paper tackled the challenge of accurately estimating the state of charge (SOC) in hybrid energy storage systems for electric vehicles, proposing a deep learning method that improved SOC estimation accuracy by 91.5% on average with errors below 0.1% and reduced consumption time by 11.4%.
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.