Optimal design of experiments for a lithium-ion cell: parameters identification of an isothermal single particle model with electrolyte dynamics
For battery management systems, this provides a more efficient way to calibrate simplified electrochemical models, improving parameter identifiability.
This work proposes a Fisher-based optimal experimental design to identify parameters of the Single Particle Model with electrolyte dynamics (SPMe) for lithium-ion cells, minimizing the covariance matrix via nonlinear optimization. The method outperforms standard strategies in both synthetic (SPMe as plant) and realistic (P2D model as plant) scenarios.
Advanced battery management systems rely on mathematical models to guarantee optimal functioning of Lithium-ion batteries. The Pseudo-Two Dimensional (P2D) model is a very detailed electrochemical model suitable for simulations. On the other side, its complexity prevents its usage in control and state estimation. Therefore, it is more appropriate the use of simplified electrochemical models such as the Single Particle Model with electrolyte dynamics (SPMe), which exhibits good adherence to real data when suitably calibrated. This work focuses on a Fisher-based optimal experimental design for identifying the SPMe parameters. The proposed approach relies on a nonlinear optimization to minimize the covariance parameters matrix. At first, the parameters are estimated by considering the SPMe as the real plant. Subsequently, a more realistic scenario is considered where the P2D model is used to reproduce a real battery behavior. Results show the effectiveness of the optimal experimental design when compared to standard strategies.