Safety-Driven Battery Charging: A Fisher Information-guided Adaptive MPC with Real-time Parameter Identification
Jorge Espin, Yuichi Kajiura, Dong Zhang
TL;DR
The paper tackles parameter identifiability challenges in Li-ion battery charging by integrating Fisher Information (FI) theory with an offline–online adaptive Model Predictive Control (MPC) framework. An offline phase optimizes a SOC trajectory to maximize the determinant of the Fisher information matrix, thereby improving parameter identifiability, while enforcing safety constraints. The online phase uses adaptive MPC to track the offline trajectory and update model parameters in real time, ensuring robust, safe charging despite parameter drift. Simulation results demonstrate that the approach yields consistent FI-driven trajectories and accurate real-time parameter convergence, suggesting practical viability for FI-guided, safe charging of Li-ion batteries.
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.