A Local Polynomial Approach to Nonparametric Estimation of the Best Linear Approximation of Lithium-Ion Battery From Multiple Datasets

TL;DR

This paper introduces a local polynomial method for nonparametric estimation of the optimal linear approximation of lithium-ion battery impedance using multiple datasets, addressing variability and experimental constraints.

Contribution

It proposes a novel local polynomial approach to estimate the best linear approximation of battery impedance from multiple datasets with nonlinear effects.

Findings

Effective estimation of linear approximation despite nonlinear distortions

Handles data from multiple experiments with varying conditions

Reduces need for extensive single-experiment data collection

Abstract

Battery short-term electrical impedance behavior varies between linear, linear time-varying, or nonlinear at different operating conditions. Data-based electrical impedance modeling techniques often model the battery as a linear time-invariant system at all operating conditions. In addition, these techniques require extensive and time consuming experimentation. Often due to sensor failures during experiments, constraints in data acquisition hardware, varying operating conditions, and the slow dynamics of the battery, it is not always possible to acquire data in a single experiment. Hence, multiple experiments must be performed. In this letter, a local polynomial approach is proposed to estimate nonparametrically the best linear approximation of the electrical impedance affected by varying levels of nonlinear distortion, from a series of input current and output voltage data subrecords of arbitrary length.