Composite Battery Electrode Blend Ratio from Full-Cell OCV
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Solution Overview
Problem
Determining the exact composition of a composite electrode in lithium-ion batteries is challenging due to the lack of access to individual components and lengthy characterization times, which hinders accurate modeling and prediction of charge/discharge behavior.
Innovation Solution
A battery system that includes a measurement circuit, a physics-based model, and an optimization table to simulate different electrode compositions, allowing for the determination of a composite electrode's blend ratio without invasive methods, and uses error comparison to adjust parameters for accurate OCV curve modeling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complete tear down of battery cell is performed to determine electrode composition, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The patent creates a virtual copy of the battery cell through a physics-based model that replicates the electrochemical behavior. Instead of physically disassembling the cell, the system simulates its operation under various conditions and compares model predictions with actual measurements to infer electrode composition. This virtual copying approach eliminates the need for time-consuming physical teardown while maintaining composition determination accuracy.
Solution Approach 2:
The patent replaces the mechanical disassembly process with an computational modeling approach. The physics-based model uses electrochemical equations to simulate cell behavior, substituting physical teardown with mathematical simulations that predict OCV curves based on hypothesized electrode compositions. This substitution transforms a mechanical destruction process into a non-invasive computational analysis.
2Loss of time
If physics-based model simulation is used to determine electrode composition, then loss of time is reduced, but measurement precision may be compromised
Solution Approach 1:
The patent implements a feedback loop where the physics-based model predictions are continuously compared with actual battery measurements. The system adjusts the hypothesized electrode composition in the model based on the difference between predicted and measured OCV curves, iteratively refining the composition estimate until convergence. This feedback mechanism ensures that the rapid simulation approach achieves composition determination accuracy comparable to physical analysis.
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
Enables efficient and non-invasive determination of composite electrode composition, reducing evaluation time and improving the accuracy of OCV curve modeling for lithium-ion batteries, thereby enhancing battery management and state-of-health estimation.
Implementation Method 1
a measurement circuit coupled to the full cell and configured to measure an actual OCV of the full cell
Implementation Method 2
The OCV is unique to a composition of the battery cell due to a dependency on thermodynamic properties of active materials found in the anode and the cathode electrodes
Implementation Method 3
Lithium-ion batteries operate based on the movement of lithium ions between a negative electrode, known as an anode electrode, and a positive electrode, known as a cathode electrode
Data Source
AI summary
A battery system includes a battery comprising a full cell having a composite electrode with a composition that includes an actual blend ratio of one or more electrode materials; a memory configured to store an optimization table; a measurement circuit coupled to the full cell and configured to measure an actual open circuit voltage (OCV) of the full cell; and at least one processor configured to: calculate a predicted OCV of the full cell that based on the optimization table and one or more optimization parameters, including a predicted blend ratio of the composite electrode, compare the actual OCV with the predicted OCV to generate an error value, compare the error value with a threshold value, determine whether the predicted blend ratio corresponds to the actual blend ratio of the composite electrode based on whether the error value satisfies the threshold value.