Machine Learning Cell Configuration for Silicon Anode Batteries
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Solution Overview
Problem
Conventional battery configuration methods are costly, cumbersome, and inefficient, limiting battery lifetime and requiring extensive time for implementation, which hinders the development of high-performance electrochemical energy storage for portable devices and electric vehicles.
Innovation Solution
A system and method utilizing machine learning and key predictors to configure cell performance by analyzing various parameters such as impedance, open circuit voltage, and coulombic efficiency, combined with a silicon-dominant anode and cathode design, to predict and enhance battery cycle life through a machine learning model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional battery configuration methods are used, then manufacturing process is established, but it is costly, cumbersome, and time-consuming
Solution Approach 1:
The patent applies preliminary action by measuring key parameters (impedance, open circuit voltage, coulombic efficiency) during the formation process itself, rather than after manufacturing is complete. This allows cycle life prediction to be performed in advance, reducing the time needed for post-manufacturing testing and validation.
Solution Approach 2:
The patent replaces conventional mechanical and empirical testing methods with a machine learning-based prediction system. By using algorithms that analyze electrical parameters during formation, the system substitutes lengthy physical testing with computational prediction, significantly reducing implementation time and cost.
2Reliability
If conventional battery configuration methods are used, then battery is manufactured, but battery lifetime is limited
Solution Approach 1:
The patent implements feedback by using machine learning models that continuously analyze measurements taken during formation (impedance, voltage, coulombic efficiency) to predict cycle life. This feedback mechanism allows for real-time assessment of battery performance potential, enabling better quality control and selection of high-lifetime batteries without complex post-manufacturing testing.
Solution Approach 2:
The patent focuses on measuring and analyzing specific electrical parameters (impedance, open circuit voltage, coulombic efficiency) during formation to predict long-term battery lifetime. By identifying and monitoring these key parameters, the system can predict battery performance without requiring complex configuration procedures or extensive testing protocols.
3Measurement precision
If extensive testing is performed to determine cycle life, then accurate performance data is obtained, but time and resources are consumed
Solution Approach 1:
The patent extracts the essential information needed for cycle life prediction directly from the formation process measurements (impedance, voltage, coulombic efficiency). By taking out only the critical parameters needed for prediction rather than performing comprehensive testing, the system maintains measurement precision while significantly reducing the time and resources required.
Solution Approach 2:
The patent applies partial action by using a subset of formation measurements to predict overall battery performance. Instead of performing complete lifecycle testing, the system uses limited measurements taken during formation (a partial action) to accurately predict long-term cycle life, thereby maintaining precision while improving manufacturing throughput.
Data Source
AI summary
Methods and systems are provided for key predictors and machine learning for configuring cell performance. One or more parameters relating to the cell may be measured, via a measurement apparatus, with the cell including a cathode, a separator, and a silicon-dominant anode, and the cell may be managed, based on the one or more parameters, with the managing including predetermining cycle life of the cell based on the one or more parameters using a machine learning model. The cell may be within a battery pack that includes a plurality of cells. The battery pack may be in an electric vehicle. At least one parameter may be measured before a formation process of the cell. At least one parameter may be measured during the formation process. At least one parameter may be measured during cycling of the cell.