An Improved Adaptive Velocity Update Particle Swarm Optimization Algorithm for Parameter Identification of Lithium-ion Battery

• Published in: 2023 3rd New Energy and Energy Storage System Control Summit Forum (NEESSC) pp. 519 - 523
• Main Authors: Xiang, Junfei, Liu, Donglei, Wang, Shunli, Wu, Fan
• Format: Conference Proceeding
• Language: English
• Published: IEEE 26.09.2023
• Subjects: Adaptation models; Integrated circuit modeling; Lithium-ion batteries; lithium-ion battery; Mathematical models; Parameter estimation; parameter identification; PSO; Simulation; State of charge
• DOI: 10.1109/NEESSC59976.2023.10349304

The accuracy of SOC estimation is placed on its model establishment of Lithium-ion batteries. Aiming to enhance the precision accuracy for parameter identification of lithium-ion batteries' equivalent circuit model (ECM), this article provides an improved adaptive velocity update PSO algorithm. Traditional PSO algorithm is frequently utilized for identifying the parameters of the ECM offline. However, the selection of control parameters affects the performance and effectiveness of this algorithm. Thus, this article proposes an adaptive velocity update formula according to the fitness value. Finally, compared with the simulation results obtained by using the Recursive Least Square (RLS) method and standard PSO algorithm for parameter identification, the results illustrate that the simulated terminal voltage errors are reduced by 85mV, and 13mV, respectively, which verifies that the improved parameter identification method has good accuracy.