Real Time Adaptive Estimation of Li-ion Battery Bank Parameters

TL;DR

This paper introduces a real-time, adaptive method for accurately estimating multiple parameters of a Li-ion battery bank during operation, enhancing battery management for electric vehicles.

Contribution

It presents a universal adaptive stabilizer technique that estimates battery parameters online without prior testing or post-processing, enabling real-time self-updating of battery models.

Findings

Fast convergence of parameter estimates in real-time

No need for offline experimentation or post-processing

Validated on a 400 V EV traction system

Abstract

This paper proposes an accurate and efficient Universal Adaptive Stabilizer (UAS) based online parameters estimation technique for a 400 V Li-ion battery bank. The battery open circuit voltage, parameters modeling the transient response, and series resistance are all estimated in a single real-time test. In contrast to earlier UAS based work on individual battery packs, this work does not require prior offline experimentation or any post-processing. Real time fast convergence of parameters' estimates with minimal experimental effort enables self-update of battery parameters in run-time. The proposed strategy is mathematically validated and its performance is demonstrated on a 400 V, 6.6 Ah Li-ion battery bank powering the induction motor driven prototype electric vehicle (EV) traction system.