Adjustable Scaling Parameters for State of Charge Estimation for Lithium-Ion Batteries Using Iterative Multiple UKFs

Hong Jianwang, Ricardo A. Ramirez-Mendoza and Jorge de J. Lozoya-Santos

Mathematical Problems in Engineering, 2020, vol. 2020, 1-14

Abstract:

In this paper, one unscented Kalman filter with adjustable scaling parameters is proposed to estimate the state of charge (SOC) for lithium-ion batteries, as SOC is most important in monitoring the latter battery management system. After the equivalent circuit model is applied to describe the lithium-ion battery charging and discharging properties, a state space equation is constructed to regard SOC as its first state variable. Based on this state space model about SOC, one state estimation problem corresponding to the nonlinear system is established. In implementing the unscented Kalman filter, state estimation is influenced by the scaling parameter. Then, one criterion function is constructed to choose the scaling parameter adaptively by minimizing this criterion function. To extend one single unscented Kalman filter with adjustable scaling parameters to multiple module estimation, one improved unscented Kalman filter is advised based on iterative multiple models. Generally, the main contributions of this paper consist in two folds: one is to introduce a selection strategy for the scaling parameter adaptively, and the other is to combine iterative multiple models and a single unscented Kalman filter with adjustable scaling parameters. Finally, two simulation examples confirm that our unscented Kalman filter with adjustable scaling parameters and its improved iterative form are better than the classical Kalman filter; i.e., our obtained SOC estimation error converges to zero.

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:4037306

DOI: 10.1155/2020/4037306

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