The battery State-Of-Charge (SOC) and parameters estimation is one of the crucial points to be addressed in the development of innovative electric/hybrid electric vehicles. Extended Kalman Filter (EKF) and Particle Filters (PF) are two possible approaches to the problem. While EKF is attractive for its computational efficiency, it may not be accurate for the non-linearity and for the uncertainties involved in the battery modelling. PF is a promising alternative, even if it is computationally more demanding. In this paper, we compare the EKF and PF performance in the dual Bayesian estimation of battery state and parameters, with particular reference to lithium batteries, showing that PF is attractive, especially in the presence of inaccurate battery models.

Comparing Particle Filter and Extended Kalman Filter for Battery State-Of-Charge Estimation

RESTAINO, Rocco;ZAMBONI, Walter
2012

Abstract

The battery State-Of-Charge (SOC) and parameters estimation is one of the crucial points to be addressed in the development of innovative electric/hybrid electric vehicles. Extended Kalman Filter (EKF) and Particle Filters (PF) are two possible approaches to the problem. While EKF is attractive for its computational efficiency, it may not be accurate for the non-linearity and for the uncertainties involved in the battery modelling. PF is a promising alternative, even if it is computationally more demanding. In this paper, we compare the EKF and PF performance in the dual Bayesian estimation of battery state and parameters, with particular reference to lithium batteries, showing that PF is attractive, especially in the presence of inaccurate battery models.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11386/3880940
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