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

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.

References in corpus (1)

• Improved Initialization for Nonlinear State-Space Modeling

Cited by in corpus (3)

• Nonlinear Double-Capacitor Model for Rechargeable Batteries: Modeling, Identification and Validation
• Retrieving highly structured models starting from black-box nonlinear state-space models using polynomial decoupling
• A Local Polynomial Approach to Nonparametric Estimation of the Best Linear Approximation of Lithium-Ion Battery From Multiple Datasets