Online Parameter Estimation of a Lithium-Ion Battery based on Sunflower Optimization Algorithm

State of charge (SOC) estimation of the battery is crucial for electric vehicle application. The accuracy of the commonly adopted estimation algorithms depends on the accuracy of the model used to describe the characteristics of the battery. Since the parameters of the battery model are functions of the SOC, temperature, and aging, all of the parameters are subject to change and need to be identified in real-time, this is referred to as online identification. We propose in this paper a new algorithm based on sunflower optimization algorithm (SFO) to extract the parameters of the model, and estimate the terminal voltage in real-time. The precision of the model parameter identification was confirmed using experimental data collected from the center for advanced life cycle engineering (CALCE) battery group conducted on the A123 battery cell under three dynamic profiles. Comparison between SFO and AFRRLS (Adaptive forgetting factor recursive least squares) is established in order to prove the effectiveness of the suggested algorithm. SFO showed better parameter identification that AFFRLS. To the best of our knowledge this is the first attempt to use SFO to estimate battery parameters in real-time, we believe there is a room of improvement to achieve better results.