Optimal Input Design for Parameter Identification in an Electrochemical Li-ion Battery Model

We consider the problem of optimally designing an excitation input for parameter identification of an electrochemical Li-ion battery model. The optimized input is obtained by solving a relaxed, convex knapsack problem. In contrast to performing parameter identification with standard test cycles, we consider the problem as designing an optimal input trajectory that maximizes parameter identifiability. Specifically, we analytically derive sensitivity equations for the electrochemical model. This approach enables parameter sensitivity analysis and optimal parameter fitting via a gradient-based algorithm. The simulation results show that the optimized inputs achieve faster parameter identification compared to standard test cycles and tighten the parameter estimation confidence intervals.