Yousef Firouz

Data-Driven Nonlinear Identification of Li-Ion Battery Based on a Frequency Domain Nonparametric Analysis

Lithium ion batteries are attracting significant and growing interest, because their high energy and high power density render them an excellent option for energy storage, particularly in hybrid and electric vehicles. In this brief, a data-driven polynomial nonlinear state-space model is proposed for the operating points at the cusp of linear and nonlinear regimes of the battery’s electrical operation, based on the thorough nonparametric frequency domain characterization and quantification of the battery’s behavior in terms of its linear and nonlinear behavior at different levels of the state of charge.

Nonparametric analysis of the short-term electrical response of Li-ion battery cells

In order to develop a complete dynamic model of a lithium ion (Li-ion) batterys electrical behaviour, which is suitable for virtual-prototyping of battery-powered systems, accurate estimation of the state of charge (SoC) and state of health (SoH) is required. This in-turn depends on the quality of the models which are used for the estimation of these quantities. Hence, even before proceeding towards the modelling step, it is important to fully characterize and understand the electrical behaviour of the battery over its full operating range, so that a flexible and accurate dynamic model can be developed. In this paper, a novel frequency domain non-parametric methodology is proposed to characterize the battery short-term electrical behaviour, in terms of: Presence of the non-linearities as well as the time-variations (non-stationary behaviour) over its full operating range. This information can later be used by battery modeller to decide on the modelling methodology.