De-Novo Lithium-Ion Battery Cathode Design Strategy Based on Electrode Particle Shape Optimization for Fast Charging
Abstract
PROJECT SUMMARY (Approved for public release)TITLE: De-Novo LiBs Cathode Design Strategy Based on Electrode Particle Shape Optimization for Fast ChargingLithium-ion batteries (LiBs) with enhanced fast charging capabilities, high energy density, and improved safety are essential for powering a wide range of military equipment, from portable electronics to electric vehicles used in combat and supporting extended missions under extreme conditions. Furthermore, ongoing research into battery safety, material innovation, and recycling can drive economic growth and promote sustainable energy. By optimizing electrode particle shape and distribution, which arecritical for electrochemical performance, this research aims to develop more efficient and reliable power sources that can significantly enhance military forces operational capabilities and readiness. Despite significant testing-based advancements in fast-charging batteries, the field remains limited, with most advancements using chemistry-driven approaches. The proposed work aims to offer anew approach for cathode design using physics-based models. The proposed work focuses on physics-based cathode design strategies integrated with machine learning (ML) and manufacturing to develop fast-charging LiBs. Specific objectives are to:1.Develop a Comprehensive Physics-Based Model for Fast Charging Cathodes: Formulate a detailed model for electrode particle behavior during fast charging using tensor calculus and physics-informed neural networks (PINNs). Rewrite and solve the continuum equations of batteries in generally curved, skew curvilinear coordinates of LiFePO4 (LFP) and LiNi1#x# yMnyCO xO2 (NMC) particles by mapping them (using bijectivepoint transformations) onto orthogonal curvilinear surfaces such as spheres, for which known solutions exist.2.Utilize Simulation Schemes (e.g., Finite Element Methods and Machine Learning): Employ finite element methods and machine learning to identify the proper orthogonal decomposition (POD) modes for the model coefficients and validate them with theoretical solutions obtained from tensor calculus. 3.Suggest Manufacturing Integrated Optimize Electrode Particle Geometries for Fast Charging: Identify optimal particle geometries that maximize charging speed while minimizing degradation. Develop design guidelines for manufacturing electrode particles optimized for fast charging, incorporating insights from both theoretical modeling and machine learning predictions.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Apr 10, 2025
- Source ID
- N000142512206
Entities
People
- Allen L Garner
Organizations
- Office of Naval Research
- Purdue University
- United States Navy