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

TL;DR

This paper introduces a data-driven polynomial nonlinear state-space model for lithium-ion batteries, utilizing frequency domain analysis to characterize their linear and nonlinear behaviors across different charge levels.

Contribution

It presents a novel approach combining nonparametric frequency domain analysis with polynomial nonlinear modeling for battery system identification.

Findings

Effective characterization of battery nonlinearities at various charge levels

Improved modeling accuracy over traditional linear methods

Potential for enhanced battery management systems

Abstract

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.