Battery impedance spectroscopy models are given by fractional-order (FO) differential equations. In the discrete-time domain, they give rise to state-space models where the latent process is not Markovian. Parameter estimation for these models is, therefore, challenging, especially for noncommensurate FO models. In this paper, we propose a Bayesian approach to identify the parameters of generic FO systems. The computational challenge is tackled with particle Markov chain Monte Carlo methods, with an implementation specifically designed for the non-Markovian setting. Lien vers l'article
JACOB, P., ALAVI, S.M.M., MAHDI, A., PAYNE, S.J. and HOWEY, D.A. (2018). Bayesian Inference in Non-Markovian State-Space Models With Applications to Battery Fractional-Order Systems. IEEE Transactions on Control Systems Technology, 26(2), pp. 497-506.