Estimating Surface Ion Density in Li-Ion Battery Electrodes
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Solution Overview
Problem
Current methods fail to accurately determine the ion density of the surface of battery electrodes, which is crucial for effective battery charging and energy storage, particularly in automotive applications.
Innovation Solution
A machine-implemented method that divides each electrode into N layers of active material, determines the battery current, and calculates ion density variables for each layer, using equations to estimate the ion density of the electrode surface based on differences between adjacent layers and battery current.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current methods are used to determine ion density, then the process is simple, but the accuracy of ion density determination is insufficient
Solution Approach 1:
The electrode is divided into multiple discrete layers (first layer, second layer, third layer, etc.) with each layer having a specific ion density variable. This segmentation allows the complex continuous ion density distribution to be approximated by discrete layer values, improving measurement precision while maintaining computational tractability.
Solution Approach 2:
The patent introduces a spatial dimension by dividing the electrode into multiple layers along the depth direction. Instead of measuring a single bulk ion density value, the method captures ion density variations across different depths (surface layer, intermediate layers, bulk layers), thereby improving accuracy by adding spatial resolution to the measurement.
2Measurement precision
If a single-layer model is used, then the calculation is simple, but it cannot capture ion density gradients across the electrode
Solution Approach 1:
The electrode is divided into multiple discrete layers (first layer, second layer, third layer, etc.) with each layer having a specific ion density variable. This segmentation allows the complex continuous ion density distribution to be approximated by discrete layer values, improving measurement precision while maintaining computational tractability.
Solution Approach 2:
The patent changes the parameter representation from a single bulk ion density value to multiple layer-specific ion density variables (C1, C2, C3, etc.). This parameter transformation enables the model to capture ion density gradients and surface effects while maintaining a manageable number of parameters through the use of recurrence relationships.
3Measurement precision
If detailed multi-layer modeling is implemented, then ion density accuracy improves, but computational complexity increases
Solution Approach 1:
The electrode is divided into multiple discrete layers (first layer, second layer, third layer, etc.) with each layer having a specific ion density variable. This segmentation allows the complex continuous ion density distribution to be approximated by discrete layer values, improving measurement precision while maintaining computational tractability.
Solution Approach 2:
The patent establishes recurrence relationships that allow continuous updating of ion density variables as current flows through the battery. The relationships enable sequential calculation from the first layer through subsequent layers, maintaining computational efficiency by avoiding the need to solve a full system of equations at each time step.
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 method provides an accurate model for battery behavior, improving energy storage by enabling precise control of battery charging, as demonstrated by its comparison to existing models like the Single Particle Battery model and experimental validation.
Implementation Method 1
The ion density variable of each of the N layers of the active electrode material is a function of the difference between the respective ion density variables of adjacent N layers
Data Source
AI summary
The present teachings are directed toward machine implemented method for estimating the ion density of the surface of either positive or negative electrode of a battery. The machine-implemented method includes dividing each electrode into N layers of active electrode material, determining the ion density variable for each one of the N layers of the active electrode, and determining the ion density of the electrode surface. In the presently disclosed method, the ion density variable of each of the N layers of the active electrode changes as a function of the difference between the respective ion density variables of adjacent N layers, and the ion density of the electrode surface changes as a function of the battery current and the difference between the respective ion density variables of adjacent N layers. The present method is particularly applicable to Li-ion batteries.