Enhanced Production Management in Energy Storage Parameter Estimation and Modeling of Lithium-Ion Batteries under Dynamic Loads

Abstract

Efficient production management in energy storage systems requires accurate performance modeling of lithium-ion batteries (LIBs), especially under varying load conditions. This study presents a novel simplified lumped parameter approach that predicts battery performance with minimal reliance on internal design specifics. The approach uses a blackbox modeling technique to estimate critical parameters—ohmic overpotential, diffusion time constant, and charge exchange current—via a Levenberg–Marquardt optimization algorithm, based on experimental voltage, current, and open circuit voltage data. Results demonstrate high accuracy in predicting cell voltage over dynamic load cycles, achieving standard deviations of 0.015 V and 0.014 V in parameter estimation and load prediction, respectively. These findings have significant implications for advancing energy storage systems by enabling more sustainable production management practices, reducing resource wastage, and improving operational efficiency. By enhancing the adaptability of production processes while maintaining high performance, this model contributes to achieving long-term goals of sustainability and scalability in energy storage applications.