Improved Gated Recurrent Unit Network with Sparrow Search Algorithm for Lithium-Ion Battery State of Health Estimation

The condition of lithium-ion batteries (LIBs), commonly referred to as the state of health (SOH), holds a critical role in ensuring the safety of the entire system. A precise estimation of SOH is crucial.This paper introduces the SSAGRU network, a battery SOH estimation method that utilizes the sparrow search algorithm and an improved gated recurrent unit. The current curve is used to extract three different features related to battery aging during the constant voltage stage, and their correlation with SOH is analyzed through grey correlation analysis. The proposed SSA-improved GRU model proves to be effective in achieving accurate and stable SOH estimation, outperforming two other algorithms tested.