Research on Two-Stage Parameter Identification for Various Lithium-Ion Battery Models Using Bio-Inspired Optimization Algorithms

Lithium-ion batteries (LIBs) are vital components in electric vehicles (EVs) and battery energy storage systems (BESS). Accurate estimation of the state of charge (SOC) and state of health (SOH) depends heavily on precise battery modeling. This paper examines six commonly used equivalent circuit models (ECMs) by deriving their impedance transfer functions and comparing them with measured electrochemical impedance spectroscopy (EIS) data. The particle swarm optimization (PSO) algorithm is first utilized to identify the ECM with the best EIS fit. Then, thirteen bio-inspired optimization algorithms (BIOAs) are employed for parameter identification and comparison. Results show that the fractional-order R(RQ)(RQ) model with a mean absolute percentage error (MAPE) of 10.797% achieves the lowest total model fitting error and possesses the highest matching accuracy. In model parameter identification using BIOAs, the marine predators algorithm (MPA) reaches the lowest estimated MAPE of 10.694%, surpassing other algorithms in this study. The Friedman ranking test further confirms MPA as the most effective method. When combined with an Internet-of-Things-based online battery monitoring system, the proposed approach provides a low-cost, high-precision platform for rapid modeling and parameter identification, supporting advanced SOC and SOH estimation technologies.