Mechanistic Model for Battery Conductance Fade Prediction
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Solution Overview
Problem
Existing battery technologies face challenges in accurately modeling and predicting the conductance characteristics of rechargeable electrochemical cells, particularly lithium-ion batteries, due to limitations in kinetic performance and impedance measurements, which affect their performance and lifespan.
Innovation Solution
A mechanistic level modeling approach using constant-current pulses at various exchange current densities to develop a system that tracks and diagnoses cell conductance, incorporating monitoring hardware and computing systems to analyze performance fade characteristics over aging, allowing for more accurate prediction and modification of cell behavior.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If constant-current pulses are applied at multiple current values bracketing exchange current density at multiple aging periods, then measurement precision of conductance characteristics is improved, but loss of time increases due to extended testing duration
Solution Approach 1:
The patent applies preliminary action by performing constant-current pulse tests at multiple current values bracketing the exchange current density at multiple aging periods (including initial and intermediate aging stages). This preliminary comprehensive characterization enables the development of a mechanistic model that can predict conductance behavior without requiring exhaustive continuous testing, thus improving measurement precision while managing time investment through strategic sampling at key aging milestones
Solution Approach 2:
The patent utilizes parameter changes by varying the current values applied during pulsing to bracket the exchange current density, and by conducting tests at different aging periods. These controlled parameter changes allow the extraction of kinetic parameters that characterize conductance fade behavior, enabling accurate prediction models that balance measurement precision with reasonable testing timeframes
2Measurement precision
If a mechanistic level model is developed using multiple current pulses at various aging periods, then prediction accuracy of performance fade is improved, but device complexity increases due to sophisticated modeling requirements
Solution Approach 1:
The patent employs an intermediary approach by developing a mechanistic level model that serves as a mediator between the complex electrochemical processes occurring within the battery and the observable conductance measurements. This model acts as an intermediary layer that translates multiple current pulse responses at different aging periods into predictive capabilities for performance fade, improving prediction accuracy while encapsulating the complexity within a manageable modeling framework
Solution Approach 2:
The patent applies parameter changes by utilizing the mechanistic model to extract and track kinetic parameters that evolve with aging. By focusing on these key parameters that change with aging conditions, the system achieves high prediction accuracy for performance fade without requiring the full complexity of all underlying electrochemical processes to be explicitly modeled and managed
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This method provides a robust and accurate means to determine, track, and predict cell conductance, enhancing the understanding and management of battery performance fade, thereby improving the design and longevity of electrochemical cells.
Implementation Method 1
the electron-accepting charge transfer reaction occurs at the cathode surface during cell discharge, and occurs at the anode surface during cell charge
Implementation Method 2
first constant-current pulses applied to the electrochemical cell at a first aging period and at three or more current values bracketing a first exchange current density
Data Source
AI summary
A system includes an electrochemical cell, monitoring hardware, and a computing system. The monitoring hardware periodically samples performance characteristics of the electrochemical cell. The computing system determines cell information from the performance characteristics of the electrochemical cell. The computing system also develops a mechanistic level model of the electrochemical cell to determine performance fade characteristics of the electrochemical cell and analyzing the mechanistic level model to estimate performance fade characteristics over aging of a similar electrochemical cell. The mechanistic level model uses first constant-current pulses applied to the electrochemical cell at a first aging period and at three or more current values bracketing a first exchange current density. The mechanistic level model also is based on second constant-current pulses applied to the electrochemical cell at a second aging period and at three or more current values bracketing the second exchange current density.