Battery Lifetime Simulation Using Adaptive Inter-Cycle Extrapolation
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Solution Overview
Problem
Current battery lifetime prediction systems are too slow for rapid feedback and repeated simulations, hindering the acceleration of battery cell development processes in lithium-ion battery manufacturing.
Innovation Solution
A method that uses a degradation model based on porous-electrode theory to sequentially calculate cell capacity at the end of each cycle, allowing for the prediction of battery end-of-life by selecting cell components and tuning parameters such as solid electrolyte interphase kinetic rate constants and active material particle cracking rates, without algebraic equations, thereby speeding up simulations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If physics-based electrochemical models are used to simulate battery lifetime, then prediction accuracy is improved, but simulation time increases to several minutes or hours
Solution Approach 1:
The battery lifetime simulation is segmented into multiple cycles, where only representative cycles are simulated in detail while other cycles are extrapolated. This divides the full lifetime simulation into manageable segments, reducing total computation time while maintaining accuracy through selective detailed modeling of critical degradation phases.
Solution Approach 2:
The model performs preliminary identification of critical cycles that contribute most to battery degradation before running full simulations. By pre-selecting which cycles require detailed physics-based modeling versus which can be extrapolated, the system prepares an optimized simulation schedule that anticipates computational needs and avoids wasting resources on non-critical phases.
2Reliability
If repeated simulations are performed for parameter estimation, then model development is improved, but computational burden increases prohibitively
Solution Approach 1:
Instead of running multiple full physics-based simulations for parameter estimation, the system creates simplified copies or surrogate models that replicate the behavior of the detailed physics model. These surrogate models can be evaluated rapidly repeated times for parameter optimization, while still capturing the essential degradation physics, thus enabling iterative model development without prohibitive computational costs.
3Loss of time
If accelerated aging conditions are used in lab testing, then testing time is reduced to a few months, but rest between cycles is skipped degrading battery realism
Solution Approach 1:
The physics-based model incorporates parameter changes that account for the effects of rest periods between cycles, even when such rests are not explicitly simulated. By adjusting degradation parameters and state variables to reflect the cumulative impact of rest phases, the model maintains realistic battery behavior under accelerated testing conditions, bridging the gap between fast lab testing and real-world battery performance.
Data Source
AI summary
Disclosed is a method for manufacturing an electrochemical cell wherein the cell undergoes degradation that results in loss of active material and cation inventory during charging phase(s) of cell cycles. The method comprises: selecting at least one cell component from electrolytes, cathode active materials, and anode active materials; sequentially calculating a cell capacity at an end of each of a plurality of cell cycles based on total cyclable cations, accessible storage sites in each electrode, and the cell component(s) using a degradation model based on porous-electrode theory and having degradation pathway(s), wherein the cell cycles are initialized based on a rate of degradation over previous cycles and wherein a time at which to simulate the next cycle is chosen based on the rate of degradation over the previous cycles; and predicting end of life of the cell based on one of the calculated cell capacities being less than a percentage of nominal capacity.