Parameter Identification for Lithium-Ion Battery Based on Hybrid Genetic–Fractional Beetle Swarm Optimization Method

Abstract

This paper proposes a fractional order (FO) impedance model for lithium-ion batteries and a method for model parameter identification. The model is established based on electrochemical impedance spectroscopy (EIS). A new hybrid genetic–fractional beetle swarm optimization (HGA-FBSO) scheme is derived for parameter identification, which combines the advantages of genetic algorithms (GA) and beetle swarm optimization (BSO). The approach leads to an equivalent circuit model being able to describe accurately the dynamic behavior of the lithium-ion battery. Experimental results illustrate the effectiveness of the proposed method, yielding voltage estimation root-mean-squared error (RMSE) of 10.5 mV and mean absolute error (MAE) of 0.6058%. This corresponds to accuracy improvements of 32.26% and 7.89% for the RMSE, and 43.83% and 13.67% for the MAE, when comparing the results of the new approach to those obtained with the GA and the FBSO methods, respectively.

State-of-Charge Estimation of Lithium-Ion Battery Based on Convolutional Neural Network Combined with Unscented Kalman Filter

“…Accurate estimation of the SOC is crucial for EVs, being used by the battery management system (BMS) to prevent over-discharging and over-charging and, ultimately, to guarantee the reliability and safe operation of EVs [7][8][9]. However, the SOC is not directly measurable, being indirectly determined through acquired sensory data, namely battery voltage, current and temperature, and some suitable data processing method [10]. Currently, commonly used methods for determining the SOC include open circuit voltage (OCV), current integration, model-based and data-driven ones.…”