On Electrochemical Model-Based State Estimation for Lithium-Sulfur Batteries

The high theoretical specific energy density of lithium-sulfur (Li-S) batteries positions them as an advanced next-generation battery system to overcome the limitations of conventional Li-ion batteries. Accurate estimation of the mass evolution of active sulfur species in Li-S cells is required, not only to monitor degradation mechanisms inside the cell but also to enable safe and efficient operation. The state estimation problem for electrochemical models (EMs) of Li-S cells is challenging, mainly due to the complex dynamics during discharge/charge processes. In this work, we consider a three-step 0-D EM with the shuttle effect for state estimation. The model's state observability is analyzed and the parameters are identified using experimental data. An extended Kalman filter is directly applied to the nonlinear differential-algebraic equation (DAE) system to estimate the differential and algebraic states from the measurements of voltage and current only. The simulation and experimental results demonstrate the effectiveness of the proposed observer design.

Automated summary

Lithium-sulfur (Li-S) batteries have the potential to overcome the limitations of conventional Li-ion batteries with their high theoretical specific energy density. This study proposes a model and observer design approach for state estimation of Li-S batteries, using experimental data for parameter identification and an extended Kalman filter for state estimation.