Battery State Estimation for a Single Particle Model With Electrolyte Dynamics

This paper studies a state estimation scheme for a reduced electrochemical battery model, using voltage and current measurements. Real-time electrochemical state information enables high-fidelity monitoring and high-performance operation in advanced battery management systems, for applications such as consumer electronics, electrified vehicles, and grid energy storage. This paper derives a single particle model (SPM) with electrolyte that achieves higher predictive accuracy than the SPM. Next, we propose an estimation scheme and prove estimation error system stability, assuming that the total amount of lithium in the cell is known. The state estimation scheme exploits the dynamical properties, such as marginal stability, local invertibility, and conservation of lithium. Simulations demonstrate the algorithm's performance and limitations.