Lithium-ion Battery State of Health Estimation on the Basis of Impedance Data Using the Collaborative Architecture of Polar-lights-optimizer-based Random Forest and Transfer Learning (Supporting Information)

A high-precision estimation method for lithium-ion battery SOH is proposed based on electrochemical impedance spectroscopy (EIS). After the characteristic parameters of impedance spectra are analyzed using a suitable equivalent circuit model, two new health factors, namely the resistance capacitance ratio (RCR) and the mid-low-frequency regional line segment (<i>L</i>_AB), are proposed.

A mathematical relationship called Bradley model is established to associate RCR with SOH while a cubic polynomial model is employed to describe the relationship between <i>L</i>_AB and SOH. A collaborative architecture of polar-lights-optimizer-based random forest and transfer learning (PLO-RF-TL) is designed to address the issues of the trap of local optimum and feature distribution shifts caused by differences in battery types and environmental temperature, thereby achieving efficient transfer of cross domain knowledge.