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DOE OSTI · 2584011

Searching for a Pulse: Evaluating the Use of Rapid DC Pulses for Diagnosing Battery Health, State-of-Charge, and Safety

Gasper, Paul [National Renewable Energy Laboratory (NREL), Golden, CO (United States)] (ORCID:0000000188349458)·Prakash, Nina [National Renewable Energy Laboratory (NREL), Golden, CO (United States)]·Knutson, Bryce [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Univ. of Colorado, Boulder, CO (United States)]·Bethel, Thomas [National Renewable Energy Laboratory (NREL), Golden, CO (United States)]·Ramirez-Meyers, Katrina [Carnegie Mellon Univ., Pittsburgh, PA (United States)] (ORCID:0000000291216952)·Condon, Amariah [Glimpse Engineering, Inc., Somerville, MA (United States)]·Attia, Peter M. [Glimpse Engineering, Inc., Somerville, MA (United States)] (ORCID:0000000347455726)·Keyser, Matthew [National Renewable Energy Laboratory (NREL), Golden, CO (United States)] (ORCID:000000026929847X)
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Abstract

Rapid electrochemical diagnostics, like DC pulse sequences or electrochemical impedance spectroscopy, are known to be useful for capacity prediction. However, it is unclear how previous results will map to different cell types and use cases and whether rapid diagnostics are useful for remaining useful life prediction or for detecting potential safety issues. To that end, we have collected a data set with ∼50,000 DC pulse measurements from four types of commercial lithium-ion batteries to enable training of state-of-charge, health, and safety diagnostic models via machine-learning. We demonstrate that 120-second DC pulse sequences can be used to predict capacity with 2%–9% average error, which can separate high- from low-capacity cells with only a 0.3% false positive rate but is not accurate enough to estimate remaining useful life. We also find that no safety related targets can be accurately predicted, highlighting the critical need for other non-invasive methods to diagnose battery safety.

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25 ENERGY STORAGE33 ADVANCED PROPULSION SYSTEMS97 MATHEMATICS AND COMPUTINGbatterydata setdegradationdiagnosticsmachine-learningopen-source

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Gasper, Paul [National Renewable Energy Laboratory (NREL), Golden, CO (United States)] (ORCID:0000000188349458), Prakash, Nina [National Renewable Energy Laboratory (NREL), Golden, CO (United States)], Knutson, Bryce [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Univ. of Colorado, Boulder, CO (United States)], Bethel, Thomas [National Renewable Energy Laboratory (NREL), Golden, CO (United States)], Ramirez-Meyers, Katrina [Carnegie Mellon Univ., Pittsburgh, PA (United States)] (ORCID:0000000291216952), Condon, Amariah [Glimpse Engineering, Inc., Somerville, MA (United States)], Attia, Peter M. [Glimpse Engineering, Inc., Somerville, MA (United States)] (ORCID:0000000347455726), Keyser, Matthew [National Renewable Energy Laboratory (NREL), Golden, CO (United States)] (ORCID:000000026929847X). 2025-06-04. Searching for a Pulse: Evaluating the Use of Rapid DC Pulses for Diagnosing Battery Health, State-of-Charge, and Safety. https://doi.org/10.1149/1945-7111%2Faddd50

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