Electrolyte Composition Screening for Battery Heat and Capacity Fade
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Solution Overview
Problem
Current methods for designing electrolytes for batteries are inefficient and rely heavily on trial-and-error, requiring extensive experimentation and failing to holistically evaluate electrolyte composition for optimal battery performance, which is time-consuming and computationally challenging.
Innovation Solution
A method and system that integrate initial material parameters, physics-based models, optimization parameters, and lower length scale models to estimate transport properties, compute a deviation index, and optimize electrolyte composition for minimal deviation, internal heat generation, and capacity fade, using a weighted objective function to iteratively refine material parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional experimental characterization methods are used to design electrolytes, then accurate transport properties and battery performance data can be obtained, but the process is expensive, time-consuming, and involves extensive trial and error
Solution Approach 1:
The patent applies preliminary action by using machine learning models to predict electrolyte transport properties and battery performance before actual experimentation. The system screens candidate electrolytes computationally using trained ML models, identifying promising candidates that can then be validated with minimal experimental testing. This preliminary computational screening dramatically reduces the number of trials needed and accelerates the electrolyte development process while maintaining accuracy.
2Reliability
If simulations at different length scales are performed to study electrolyte transport properties and battery performance, then comprehensive understanding can be achieved, but the computational time varies and combination is challenging
Solution Approach 1:
The patent merges multiple length-scale simulations and machine learning approaches into a unified framework. The system integrates atomistic simulations, molecular dynamics, and continuum-level battery models, combining their outputs to train comprehensive ML models. This unified approach allows the system to leverage insights from different scales simultaneously, achieving reliable performance predictions while managing computational complexity through the coordinating mechanism.
3Productivity
If machine learning methods are used to screen electrolyte candidates based on individual transport properties, then high-throughput screening is achieved and development time is reduced, but the influence of electrolyte on overall battery performance is not addressed
Solution Approach 1:
The patent implements universality by creating a multi-functional machine learning system that simultaneously evaluates multiple electrolyte properties and their combined impact on battery performance. The ML models are trained to predict not only individual transport properties like ionic conductivity but also overall battery metrics including capacity, energy density, and cycle life. This universal approach allows high-throughput screening while comprehensively assessing battery performance implications.
Data Source
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AI summary
The present invention relates to the field of electrolyte design. Existing methods focus on optimizing the electrolyte composition on a stand-alone basis with respect to its properties and validating battery performance experimentally which is a time-consuming process. Thus, embodiments of present disclosure provide an automated method and system for identifying electrolyte composition for optimal battery performance. The system receives certain input parameters and computes transport properties using the input. Then, a feasible electrolyte composition is identified from a material database based on deviation index metric. The identified electrolyte composition is then optimized based on the input by considering the deviation index and battery performance metrics such as capacity fade and internal heat generation. Simulation case studies performed show that the method is capable of identifying a new electrolyte from the material database as well as identify optimal concentration of same electrolyte which results in better performance of the battery.