Informative experiment design for parameter estimation of the Doyle–Fuller–Newman battery model

Estimating the parameters of physics-based battery models is a challenging problem, due to the large number of model parameters and the poor parameter sensitivity of some of them. This paper proposes a procedure to design experiments for the Doyle–Fuller–Newman (DFN) model that maximises the parameter sensitivities. We consider a carefully selected class of input signals whose hyperparameters are optimised using an informativity index that uses the Fisher information matrix. As the Fisher information matrix relies on a local parameter sensitivity analysis evaluated at a nominal operating point, the effects of the parameter bounds on this sensitivity analysis is also studied. The design procedure leads to an improved model accuracy when compared to estimating the parameters using constant-current experiments or a drive cycle.