A Curve Relocation Approach for Robust Battery Open Circuit Voltage Reconstruction and Capacity Estimation Based on Partial Charging Data

This article proposes a curve relocation approach for robust battery open circuit voltage (OCV) reconstruction and capacity estimation based on partial charging data. First, an electrode-level aging mechanism analysis is conducted to reveal the underlying reasons for battery OCV distortion and capacity decay, and three electrode aging parameters (EAPs) are proposed to account for those aging-induced relative position shifts of electrode OCV curves. Second, a deep long short-term memory recurrent neural network with a many-to-one structure is established to yield battery OCV estimations in high fidelity using accessible daily charging data. Then, a multicoupled optimization algorithm is designed to accurately estimate EAPs, which ensures that the reconstructed OCV curves match well with the estimated OCV segments while satisfying various OCV-related health features at a specific aging level. Obtaining the optimal EAPs contributes to 1) relocate the relative positions of electrode OCV curves for reliable battery OCV reconstruction; 2) determine battery usable capacity range and achieve capacity estimation in high accuracy; and 3) help understand electrode aging behaviors. The proposed method realizes a mean absolute error of less than 20 mV in battery OCV reconstruction and a mean absolute percentage error of less than 1.3% in capacity estimation given a short charging segment up to 1000 s over the whole battery lifetime.