LAUSR

Abstract Accurate parameter identification and state of charge (SoC) estimation of a Li-ion battery are a critical issue for efficient monitoring, control and utilization of advanced battery management system (BMS) in electric vehicles. The existing single-particle model with electrolyte dynamics (SPMe) can accurately describe the Li-ion cell's macro-micro scale dynamic behaviors, but they are vulnerable as predictive tools under specific operating conditions, which is more prone to erroneous estimation results. To overcome this problem, this paper proposes a parameter identification scheme with its systematic validation for an enhanced electrochemical aging model of the cylindrical 18650 Li-ion nickel manganese cobalt oxide (NMC) cell under the typical experimental testing scenarios. First, a SPMe-based aging degradation model is developed through including Solid-Electrolyte-Interface (SEI) layer formation at the negative electrode, denoted as eSPMA model. Next, the dozens of parameters in this eSPMA model are evaluated using the conventional genetic algorithm and the validation of these identified parameters is conducted under different operating conditions, and simultaneously, this eSPMA model is used to estimate Li-ion batteries SoC under the same operation scenarios. Finally, the effectiveness of the identified parameter is validated by a comparative study of the simulated and experimental voltage and SoC under the same input current profile based on the eSPMA and the Doyle–Fuller–Newman (DFN) model. The results of this study can assist in the experiment design of battery capacity degradation and facilitate the accurate estimation of state-of-health for advanced battery management system in the electrified vehicle applications.