Lie Generator Networks Extract EIS-Grade Battery Diagnostics from Pulse Relaxation Data
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arXiv
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| Format: | Preprint |
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2026
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| author | Jamil, Shafayeth Kapadia, Rehan |
| author_facet | Jamil, Shafayeth Kapadia, Rehan |
| contents | Electrochemical impedance spectroscopy (EIS) is the most informative diagnostic for lithium-ion batteries: its frequency-resolved spectra decompose cell behavior into distinct electrochemical processes, revealing mechanism-specific degradation invisible to voltage and resistance measurements. Yet EIS requires dedicated hardware and minutes-long acquisitions incompatible with field deployment. Here we show that Lie Generator Networks (LGN), a structure-preserving identification framework, extract electrochemical time constants from 60 seconds of post-pulse voltage relaxation, data that battery management systems already collect, that encode the same diagnostic and prognostic information as impedance spectra. LGN learns the generator matrix of the relaxation dynamics with stability guaranteed by architecture, yielding time constants precise enough to resolve electrochemical variation that conventional curve fitting cannot detect from identical data. Across five datasets totaling over 850 cells, four institutions, and multiple chemistries, LGN tracks degradation with near-perfect rank correlation ($|ρ_s| = 0.999$), enables cross-validated reconstruction of full Nyquist spectra at 2% median error across 227 cells, predicts which capacity-matched cells fail first from three early diagnostics, and recovers Arrhenius activation energies with zero physics priors without retraining or cell-specific tuning. LGN requires no training data, no impedance hardware, and no chemistry-specific calibration, converting any existing relaxation pulse into an impedance-grade diagnostic. This enables real-time health monitoring, rapid second-life grading, production-line quality control, and physics-informed prognosis from minutes of measurement. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_15351 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Lie Generator Networks Extract EIS-Grade Battery Diagnostics from Pulse Relaxation Data Jamil, Shafayeth Kapadia, Rehan Systems and Control Electrochemical impedance spectroscopy (EIS) is the most informative diagnostic for lithium-ion batteries: its frequency-resolved spectra decompose cell behavior into distinct electrochemical processes, revealing mechanism-specific degradation invisible to voltage and resistance measurements. Yet EIS requires dedicated hardware and minutes-long acquisitions incompatible with field deployment. Here we show that Lie Generator Networks (LGN), a structure-preserving identification framework, extract electrochemical time constants from 60 seconds of post-pulse voltage relaxation, data that battery management systems already collect, that encode the same diagnostic and prognostic information as impedance spectra. LGN learns the generator matrix of the relaxation dynamics with stability guaranteed by architecture, yielding time constants precise enough to resolve electrochemical variation that conventional curve fitting cannot detect from identical data. Across five datasets totaling over 850 cells, four institutions, and multiple chemistries, LGN tracks degradation with near-perfect rank correlation ($|ρ_s| = 0.999$), enables cross-validated reconstruction of full Nyquist spectra at 2% median error across 227 cells, predicts which capacity-matched cells fail first from three early diagnostics, and recovers Arrhenius activation energies with zero physics priors without retraining or cell-specific tuning. LGN requires no training data, no impedance hardware, and no chemistry-specific calibration, converting any existing relaxation pulse into an impedance-grade diagnostic. This enables real-time health monitoring, rapid second-life grading, production-line quality control, and physics-informed prognosis from minutes of measurement. |
| title | Lie Generator Networks Extract EIS-Grade Battery Diagnostics from Pulse Relaxation Data |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2605.15351 |