Data-Selective Online Battery Identification Using Extended Time Regular Expressions

TL;DR

This paper introduces an online battery identification method that selectively uses vehicle driving pattern data segments via Extended Time Regular Expressions, achieving accurate estimates with minimal data usage.

Contribution

The paper presents a novel data-efficient online battery identification approach utilizing ETRE to target informative data segments based on driving patterns.

Findings

Uses only 10.71% of data on average

Provides low-bias and low-variance estimates

Outperforms conventional algorithms in noisy conditions

Abstract

In this paper, we propose a data-efficient online battery identification method which targets highly informative battery cell data segments based on the driving pattern of the vehicle. We consider the case of a vehicle driving on/off a motorway and construct an Extended Time Regular Expression (ETRE) to detect data segments fitting these driving patterns. Simulation results indicate that by only using up to 10.71% of the data on average, the proposed method provides a low-bias and low-variance estimator under non-negligible current and voltage noise compared to other conventional estimation algorithms.