Safety-Driven Battery Charging: A Fisher Information-guided Adaptive MPC with Real-time Parameter Identification

TL;DR

This paper introduces a Fisher Information-guided adaptive Model Predictive Control method for lithium-ion battery charging, improving real-time parameter estimation and safety by addressing model parameter identifiability issues.

Contribution

It integrates Fisher Information theory with MPC to enhance parameter estimation and safety in battery charging, a novel approach in battery management.

Findings

Improved parameter identifiability during charging.

Enhanced safety and performance of battery management.

Effective real-time parameter estimation demonstrated in simulations.

Abstract

Lithium-ion (Li-ion) batteries are ubiquitous in modern energy storage systems, highlighting the critical need to comprehend and optimize their performance. Yet, battery models often exhibit poor parameter identifiability which hinders the development of effective battery management strategies and impacts their overall performance, longevity, and safety. This manuscript explores the integration of Fisher Information (FI) theory with Model Predictive Control (MPC) for battery charging. The study addresses the inherent hurdles in accurately estimating battery model parameters due to nonlinear dynamics and uncertainty. Our proposed method aims to ensure safe battery charging and enhance real-time parameter estimation capabilities by leveraging adaptive control strategies guided by FI metrics. Simulation results underscore the effectiveness of our approach in mitigating parameter identifiability issues, offering promising solutions for improving the control of batteries during safe charging process.