Lithium-Silicon Battery SOH Modeling Using Multi-Source Data
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Solution Overview
Problem
Conventional state-of-health (SOH) models for lithium-ion batteries, particularly those with silicon-dominant anodes, are limited by their reliance on data from the battery itself and fail to accurately predict SOH due to hysteresis in SOC vs. voltage relationships, complex DCIR correlations, and differing degradation factors compared to graphite-based batteries.
Innovation Solution
Enhanced SOH models that incorporate additional data sources and advanced processing techniques, such as machine learning, to optimize SOH assessment, accounting for unique characteristics of silicon-dominant batteries, including manufacturing and operational data, and using data from prescribed measurement sequences like current pulses and hybrid pulse power characterization tests.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional SOH models use only data from the battery itself, then the model simplicity is maintained, but the SOH estimation accuracy deteriorates due to hysteresis and complex DCIR correlations
Solution Approach 1:
The patent combines multiple data sources including battery operational data, manufacturing data, and environmental data into a unified SOH assessment model. This merging of diverse data streams enables more accurate SOH estimation by compensating for the limitations of individual data sources, particularly addressing hysteresis effects and complex DCIR correlations that cannot be captured by single-source data
Solution Approach 2:
The patent introduces machine learning algorithms as intermediary processing layers that transform raw multi-source data into meaningful SOH indicators. These algorithms act as mediators between the complex input data and the final SOH assessment, automatically handling the complexity of data integration and analysis while providing accurate predictions
2Measurement precision
If conventional models use standard measurement protocols, then the ease of operation is maintained, but the measurement precision deteriorates for silicon-dominant batteries due to unique degradation characteristics
Solution Approach 1:
The patent applies localized measurement strategies tailored to silicon-dominant battery characteristics. Instead of using universal measurement protocols, the system implements specific measurement sequences and protocols optimized for silicon anode behavior, including adjusted current pulse durations and voltage threshold settings that account for silicon's unique expansion and degradation patterns
Solution Approach 2:
The patent implements dynamic measurement protocols that adapt to the battery's state of charge and operational conditions. The measurement sequences are adjusted in real-time based on battery response, allowing the system to optimize measurement precision for silicon-dominant batteries while maintaining operational feasibility through automated adaptation
Data Source
AI summary
Systems and methods are provided for state-of-health models for lithium-silicon batteries. State-of-health (SOH) of a lithium-ion cell may be assessed, with the assessing including calculating the state-of-health (SOH) using an enhanced state-of-health (SOH) model, with the enhanced state-of-health (SOH) model using input data other than data provided directly by the lithium-ion cell. The input data includes at least data acquired during operation of the lithium-ion cell and/or data acquired during manufacturing and initialization of the lithium-ion cell or electrodes of the lithium-ion cell. The lithium-ion cell may be a silicon-dominant cell including a silicon-dominant anode with silicon >50% of active material of the anode, and the enhanced state-of-health (SOH) model may be configured based on one or more characteristics unique to silicon-dominant cells.