Lithium-ion capacitor degradation prediction via multiphysics modeling
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Solution Overview
Problem
Current methods fail to accurately predict the long-term degradation of lithium-ion capacitors (LICs) under dynamic charge or discharge profiles, which is crucial for optimizing their performance and extending their cycle life.
Innovation Solution
A system and method that utilize a multiphysics-based approach, incorporating a Randles equivalent circuit and the Arrhenius equation, to model the interaction of operating current with the electrolyte and electrodes in LICs, allowing for the prediction of degradation based on electrolyte concentration and cycle time, thereby optimizing energy storage device design and reducing development costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If experimental testing is used to study LIC performance, then insight into device behavior is gained, but time and cost for degradation prediction increase significantly
Solution Approach 1:
The patent creates a virtual copy of the LIC device through computational modeling. The multiphysics model replicates the electrochemical behavior, degradation mechanisms, and performance characteristics of physical LICs without requiring actual device fabrication and testing. This virtual replica enables degradation prediction under various operating conditions without the time and cost of physical experimentation.
Solution Approach 2:
The patent performs preliminary degradation analysis through computational simulations before physical prototyping. By using the multiphysics model to predict degradation patterns, SEI growth, and capacity fade under different cycling conditions, the system identifies optimal design parameters and operating conditions in advance, reducing the need for extensive physical testing iterations.
2Reliability
If physical prototyping is used to validate LIC performance, then accurate performance data is obtained, but development costs and iteration time increase
Solution Approach 1:
The patent uses computational models to generate virtual performance data that accurately reflects physical device behavior. The multiphysics simulations reproduce electrochemical reactions, mass transport, and degradation mechanisms with sufficient accuracy to guide design decisions, reducing reliance on expensive physical prototypes for validation.
Solution Approach 2:
The patent performs preliminary performance validation through computational testing before manufacturing physical prototypes. The model predicts device behavior under various loading conditions, temperatures, and cycling regimes, allowing designers to optimize parameters and identify potential issues before committing to expensive fabrication and testing cycles.
3Measurement precision
If detailed multiphysics modeling is implemented, then degradation prediction accuracy improves by over 85%, but computational complexity increases
Solution Approach 1:
The patent divides the complex electrochemical system into distinct physical domains that can be modeled separately and coupled together. The multiphysics model segments the LIC into electrode regions, electrolyte phases, and interface zones, with each domain governed by specific partial differential equations. This segmentation allows the complex system to be solved through modular computational approaches while maintaining high accuracy in degradation prediction.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables highly accurate predictions of energy storage device degradation, improving long-term performance modeling by over 85% compared to existing tools, and significantly reduces the time and cost associated with LIB prototyping.
Implementation Method 1
generating a Randles equivalent circuit that models how the operating current interacts with at least one of an electrolyte, lithium ions, or electrodes
Implementation Method 2
modeling, via an Arrhenius equation, irregular charge or discharge profiles of at least one of a LIB, LIC, or hybrid over time
Data Source
AI summary
Systems, methods, and media for determining an energy storage device state of health are disclosed herein. The system can determine a first cycle time for an energy storage device; determine a first energy storage capacity for the energy storage device; determine a first electrolyte concentration for the energy storage device at the first cycle time; determine a second cycle time for the energy storage device; determine a second electrolyte concentration for the energy storage device at the second cycle time; and determine a degradation rate indicating an expected degradation of the energy storage device per cycle time unit based at least on: a first difference between the first electrolyte concentration and the second electrolyte concentration; and a second difference between the first cycle time and the second cycle time.