Partition-based Unscented Kalman Filter for Reconfigurable Battery Pack State Estimation using an Electrochemical Model

TL;DR

This paper introduces a partition-based unscented Kalman filter for accurate and efficient state estimation in reconfigurable lithium-ion battery packs, leveraging a detailed electrochemical model and distributed sensor network.

Contribution

It develops a novel distributed UKF framework that improves computational efficiency for large-scale battery pack state estimation.

Findings

Distributed UKF outperforms centralized UKF in computation time.

The method maintains low mean-square estimation error.

Validation on a six-cell battery pack shows effectiveness.

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.