Bayesian Inference in Non-Markovian State-Space Models With Applications to Battery Fractional-Order Systems

  title={Bayesian Inference in Non-Markovian State-Space Models With Applications to Battery Fractional-Order Systems},
  author={Pierre E. Jacob and Seyed Mohammad Mahdi Alavi and Adam Mahdi and Stephen John Payne and David A. Howey},
  journal={IEEE Transactions on Control Systems Technology},
  • P. Jacob, S. Alavi, +2 authors D. Howey
  • Published 2018
  • Computer Science, Mathematics
  • IEEE Transactions on Control Systems Technology
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… Expand
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