Postprocessing the outputs of an interacting multiple-model Kalman filter using a Markovian trellis to estimate parameter values of aged Li-ion cells

Abstract Lithium-ion battery modeling for use in battery management systems requires models that can adapt to the changing behavior of a cell as it ages. One method to enable this adaptivity is to select the most representative model from a set of “pre-aged” models that represent the characteristics of the cell as different cyclic and calendar aging processes occur. By modeling the aging of a cell as a Markovian process, an interacting multiple-model Kalman filter (IMM) can be utilized to determine a time-varying probability mass function that specifies the probability that each of the models under consideration is the best representation of the cell under observation. While the output of the IMM is useful by itself, its predictions can be improved by post-processing. In this paper, we present methods to analyze the time-series probability mass function produced by the IMM using the Viterbi and BCJR algorithms in common use in the digital-communications discipline. These algorithms seek to identify the “best path” through the space of available models over time, based on the likelihoods produced by the IMM and the Markovian transition probabilities. Through the use of these post-processing algorithms, confidence in the best-fitting model can be improved.