A Novel Capacity Estimation Method for Lithium-Ion Batteries Based on the Adam Algorithm

Accurate estimation of the capacity of lithium-ion batteries is crucial for battery management and secondary utilization, which can ensure the healthy and efficient operation of the battery system. In this paper, we propose multiple machine learning algorithms to estimate the capacity using the incremental capacity (IC) curve features, including the adaptive moment estimation (Adam) model, root mean square propagation (RMSprop) model, and support vector regression (SVR) model. The Kalman filter algorithm is first used to construct the IC curve, and the peak and corresponding voltages correlated with battery life were analyzed and extracted as capacity estimation features. The three models were then used to learn the relationship between aging features and capacity. Finally, the lithium-ion battery cycle aging data were used to validate the capacity estimation performance of the three proposed machine learning models. The results show that the Adam model performs better than the other two models, balancing efficiency and accuracy in the capacity estimation of lithium-ion batteries throughout the entire lifecycle.