Multi-particle Reduced Order Model for Real-time Lithium Plating Prediction
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Solution Overview
Problem
Current lithium-ion battery charging methods lead to accelerated capacity fading and safety issues due to lithium metal plating, and existing models are not practical for real-time, accurate prediction and prevention of lithium plating.
Innovation Solution
A multi-particle reduced order model is used to predict lithium plating potential in real time by iteratively determining current density and potential distributions, allowing for derating of charging current to minimize lithium plating, which is more computationally efficient and accurate than single particle models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If fast charging is applied to lithium-ion batteries, then charging speed is improved, but lithium metal plating occurs causing capacity fading and safety issues
Solution Approach 1:
The system performs preliminary assessment of lithium plating potential using a reduced-order model before charging begins, and continuously monitors during charging. By predicting plating risk in advance and adjusting charging current proactively, the system prevents lithium metal plating before it causes capacity fading or safety issues, while still enabling fast charging when conditions are favorable
2Measurement precision
If extensive lithium plating tests are conducted to determine maximum charging current limits, then accuracy of plating prevention is improved, but real-time usability and computational efficiency deteriorate
Solution Approach 1:
The invention extracts only the essential variables needed for plating prediction from the full battery model, creating a reduced-order model that focuses on the critical relationships between charging current, temperature, and plating potential. This extracted simplified model maintains sufficient accuracy for real-time control decisions while eliminating unnecessary computational complexity, enabling implementation in practical battery management systems
Solution Approach 2:
The system changes the mathematical parameters and equations used in the battery model, transitioning from a detailed full-order model with many state variables to a reduced-order model with fewer key parameters. This parameter simplification reduces computational burden while preserving the essential physics of lithium plating, making the model suitable for real-time control applications
Data Source
AI summary
A multiple particle reduced order model to adjust charging applied to a load based on accurately predicting lithium plating potential in real time during the life of a lithium battery cell. In the current multi-particle reduced order modeling system, the current density and the potential distributions are solved iteratively. Once the current distribution is solved, lithium concentration distribution is solved without involving any iterative process. By solving the lithium concentration distribution as a separate step after the iteratively determined current density and potential distributions, the computation time required by the model to generate an output is dramatically reduced by avoiding solving multiple partial derivative equations iteratively. Based on the potential distribution information provided by the output of the model, lithium plating potential can be determined, and actions can be taken, such as modified charging techniques and rates, to minimize future lithium plating.