Reparodynamics Battery Validation: RYE Analyzer on a Lithium Ion Degradation Dataset
Year: 2025
Type: article
Abstract: This record contains a negative control validation study for Reparodynamics using the RYE Analyzer on a public lithium ion battery degradation dataset. The experiment applies the Repair Yield per Energy (RYE) metric and related Reparodynamic indicators to three cylindrical cells, labelled B5, B6, and B7, from the “Lithium ion battery degradation dataset” published on Kaggle by user programmer3. The RYE Analyzer converts the archive into a per cycle table with a repair relevant variable (state of health), an energy proxy derived from charge related columns, and a per cycle repair yield RYE_t = (R_t − R_{t−1}) / E_t. It then reports global statistics, resilience, a collapse index, multi domain RYE trajectories, and a composite Self Repair Index gauge. For these cells the mean RYE is about 0.1586, resilience is zero, the fraction of nonzero and positive yields is about 0.22, and the collapse index is 68. The RYE histogram is sharply peaked at zero, and the phase classifier labels the system as collapse. These findings support the interpretation that battery condition drifts downward under load without entering a self repair regime. The study serves as an external validation of the RYE Analyzer on energy storage data and as a template for future Reparodynamic battery experiments.
Author: Jenkins, Cody Ryan
Language: English
Subfield: Automotive Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7106218498
FWCI:
Cites:
Cited by:
Related to: