SOC and SOH Joint Estimation of Lithium-Ion Batteries Under Dynamic Current Rates Based on Machine Learning

It is critical to accurately estimate the state of charge (SOC) and state of health (SOH) of lithium-ion batteries to ensure the safety and reliability of marine power systems, where the inherent symmetry of lithium-ion battery charge–discharge dynamics is often disrupted. However, the accuracy of conventional methods significantly deteriorates under dynamic current rates induced by fluctuating electrical loads, leading to unreliable SOC and SOH estimates. This article proposes a novel SOC and SOH joint estimation method based on a long short-term memory network with a rate awareness attention mechanism (RAAM-LSTM) and support vector regression optimized by greylag goose algorithm (GGO-SVR). RAAM-LSTM improves SOC estimation accuracy by adaptively weighting enhanced rate-related features. For SOH estimation, the GGO-SVR model incorporates the SOC as a coupling feature and applies physical constraints to ensure consistency with irreversible battery degradation. The comparative experimental results show that the error of the SOC is less than 1.6%, and that of the SOH is less than 0.5%, which are much smaller compared with those of conventional methods.