Battery Aging Profiles From Discrete Interaction Modeling
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Solution Overview
Problem
Current methods fail to accurately analyze and predict the aging of systems, such as batteries, due to the complex interactions between transient active agents and transient events on static or semi-static populations, leading to inefficiencies in performance and lifespan management.
Innovation Solution
A method and apparatus that define and model discrete interactions between populations and agents or events, generating an aging profile indicative of changes over time, allowing for the prediction of system performance and lifespan through computational modeling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If computational modeling is used to analyze discrete interactions between transient active agents and static populations, then measurement precision of aging processes is improved, but device complexity increases
Solution Approach 1:
The system segments the complex aging analysis into discrete interaction events between transient active agents and static population members. Each interaction is modeled as an independent stochastic event, allowing the overall aging process to be constructed from simpler, manageable components rather than treating it as a monolithic complex process.
Solution Approach 2:
The patent introduces a computational modeling framework as an intermediary layer between the physical aging process and the analysis output. This framework uses virtual representations of interactions to bridge the gap between complex physical phenomena and measurable aging metrics, enabling precise analysis without directly observing every molecular interaction.
2Reliability
If detailed modeling of discrete interactions is performed, then reliability of aging predictions is improved, but loss of time for computation increases
Solution Approach 1:
The system performs preliminary actions by pre-defining the rules and parameters for discrete interactions between transient active agents and static population members before running the simulation. Interaction mechanisms, rate constants, and population characteristics are established in advance, allowing the actual aging simulation to proceed efficiently without real-time calculation of fundamental parameters.
Solution Approach 2:
The patent implements partial action by modeling only the most significant discrete interactions that contribute to aging, rather than attempting to simulate every possible interaction. The system identifies and models key interaction types (e.g., TAA-SP interactions causing deactivation) while ignoring less impactful processes, achieving reliable predictions with reduced computational burden.
3Adaptability or versatility
If the system models multiple transient active agents and events simultaneously, then adaptability of the analysis framework is improved, but device complexity increases
Solution Approach 1:
The patent creates a universal modeling framework that can handle multiple types of transient active agents (chemical species, physical events) and static population members through a single unified approach. The same discrete interaction modeling methodology applies regardless of the specific agent or event type, allowing the system to adapt to different aging scenarios without requiring separate specialized models for each case.
Data Source
AI summary
Embodiments disclosed herein include a method of analyzing changes in a system that occur over time. A battery is an example of such a system. The changes may result from discrete interactions. The method may include defining an electrode of a battery. The method may also include obtaining an expression for discrete interactions between the electrode and one or more of a solvent, a salt component, and an event that affects the battery. The method may also include modeling the discrete interactions between the electrode and the one or more of the solvent, the salt component, and the event. The method may also include obtaining, based on the modeling of the discrete interactions, an aging profile. The aging profile may be indicative of changes in the battery resulting from the discrete interactions.