Title: Partition-based Unscented Kalman Filter for Reconfigurable Battery Pack State Estimation using an Electrochemical Model,
Abstract: Accurate state estimation of large-scale lithium-ion battery packs is
necessary for the advanced control of batteries, which could potentially
increase their lifetime through e.g. reconfiguration. To tackle this problem,
an enhanced reduced-order electrochemical model is used here. This model allows
considering a wider operating range and thermal coupling between cells, the
latter turning out to be significant. The resulting nonlinear model is
exploited for state estimation through unscented Kalman filters (UKF). A sensor
network composed of one sensor node per battery cell is deployed. Each sensor
node is equipped with a local UKF, which uses available local measurements
together with additional information coming from neighboring sensor nodes. Such
state estimation scheme gives rise to a partition-based unscented Kalman filter
(PUKF). The method is validated on data from a detailed simulator for a battery
pack comprised of six cells, with reconfiguration capabilities. The results
show that the distributed approach outperforms the centralized one in terms of
computation time at the expense of a very low increase of mean-square
estimation error.