Physics-based modeling and control of secondary batteries and related electrochemical technologies

Abstract

The overarching objectives of the proposed research are to advance battery technology by developing and applying physics-based modeling and simulation. In broad terms, three closely integrated objectives can be organized as#Extend the formulation and implementation of physics-based models, #Develop a predictive understanding of battery degradation mechanisms,#Develop model-predictive-controltheory and algorithms.Broadly speaking, the physics-based modeling follows two avenues. One is based on extensions of pseudo-two-dimensional (P2D) models, which are computationally efficient but rely simplifying assumptions. Another avenue is concerned with chemo-mechanical behaviors within polycrystalline electrode structures. These models are computationally expensive, but can represent electrochemical, transport, and structural behaviors at the lattice scale. The model-predictive control (MPC) research relies on reduced-order models (ROM) that can be exercised within battery-management systems (BMS). The locally linear ROMs are extracted from physics-based models using impulse-response theory. An important aspect of the research is controlling degradation mechanisms (e.g., Li plating) that are not directly measurable, and thus rely on sensor inferences. Although the initial focus is on state-of-the-art lithium-ion batteries, the fundamental underpinning theories and model developments are extendable to alternative battery chemistries and architectures as well as to other electrochemical-based technologies such as fuel cells of electrolyzers. This abstract ispublicly releasable.

Document Details

Document Type
DoD Grant Award
Publication Date
Jan 13, 2025
Source ID
N000142512078

Entities

People

  • Robert J. Kee

Organizations

  • Colorado School of Mines
  • Office of Naval Research
  • United States Navy

Tags

Readers

  • Battery Technology and Engineering
  • Computational Fluid Dynamics (CFD)
  • Theoretical Analysis.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • Biotechnology