Li-ion battery dynamics model parameter estimation using datasheets and particle swarm optimization

Dynamics modeling is of fundamental importance to Li-ion battery design and manufacturing. Accurate dynamics models established can be used to optimize the operation strategy, manage the life cycle, and thus ensure the economic and safe operation of the batteries. The Li-ion dynamics model examined in this paper has integrated both empirical and electrochemical aspects, and it has been specifically validated for electric vehicle applications using experimental data. Previously, the model parameter estimation was done by manually picking three key points from the discharge curve obtained from the datasheet. The approach is quite subjective and error prone. The resulted model may deviate greatly from the experimental curve. To address this issue, this paper proposes to use particle swarm optimization to more objectively estimate the model parameters. Results from case studies show that the proposed approach provides more accurate estimation of the true parameters, and thus the new approach can more precisely capture the battery dynamics. In addition, this approach is generic because it is independent of specific battery chemistries and applicable to many different types of Li-ion batteries.