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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

GMS Station SOH Monitoring Configuration Guide (V.1.2)

The GMS Station State-of-Health (SOH) monitoring capability provides the system controller the ability to view current SOH values and calculated statistics for stations and channels, view trend plots of SOH values, be notified when station SOH status changes, and acknowledge or quiet notifications while the station issues are being investigated. The SOH monitoring capability includes components to acquire CD 1.1 protocol station data, extract SOH information from the raw data packets, process the raw SOH information for display, store the SOH information, and display the SOH information in an interactive display. All these components use system and processing configuration to provide the system controller mission-relevant information about station health. This document is a guide to setting the processing configuration for GMS SOH monitoring.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

GMS Station SOH Monitoring Users Guide (V.1.2)

The Geophysical Monitoring System (GMS) State-of-Health User Interface (SOH UI) is a web-based application that allows a user to view and acknowledge the SOH status of stations in the GMS system. The SOH UI will primarily be used by the System Controller, who monitors and controls the system and external data connections. The System Controller uses the station SOH UIs to monitor, detect, and troubleshoot problems with station data availability and quality.

58 GEOSCIENCES↗
DOE OSTI2026

Modeling Multi-View Impedance-Based Cross-Geometry SOH Estimator for Li-ion Batteries

Abstract: Accurately estimating battery’s State of Health (SOH) remains challenging when models must generalize across cell designs and operating conditions. Most Electrochemical Impedance Spectroscopy (EIS)-based approaches either (i) hand-engineer a few Nyquist-plot features for shallow models—fast but does not generalize across geometries—or (ii) learn directly from Nyquist plots with deep networks, which removes manual feature extraction, yet still limited to a single plot type. As a result, cross-geometry robustness and deployability on constrained Internet of Things (IoT) devices remain open problems. We propose a compact Convolutional Neural Network (CNN) (∼ 10k parameters) that takes multi-representation EIS inputs—Nyquist (real/imaginary) and phase–magnitude (|Z|/ϕ) stacked as four channels, so the model can learn complementary degradation signatures while remaining small enough for fast inference. We build a dataset from cyclic aging of two geometries (LG INR18650MJ1 cylindrical cells and LIR2032 coin cells), acquire EIS every ten cycles from 10 kHz to 10 mHz (10 points/decade), and evaluate with leave-one-cell-out testing strategy. We further study fusion vs. single-representation inputs and assess feasibility for on-device deployment (e.g., NVIDIA Jetson device). The results show that training on multiple EIS representations improves SOH estimation accuracy and cross-geometry generalization compared to single-representation models, which uses only Nyquist or phase–magnitude plots. This design targets accurate, generalizable SOH prediction without manual feature engineering while enabling practical real-time use.

Bakr, Ahmed [The University of Alabama (UA)]↗
DOE OSTI2020

Apparatus and method for determining state of change (SOC) and state of health (SOH) of electrical cells

Systems and methods for prediction of state of charge (SOH), state of health (SOC) and other characteristics of batteries using acoustic signals, includes determining acoustic data at two or more states of charge and determining a reduced acoustic data set representative of the acoustic data at the two or more states of charge. The reduced acoustic data set includes time of flight (TOF) shift, total signal amplitude, or other data points related to the states of charge. Machine learning models use at least the reduced acoustic dataset in conjunction with non-acoustic data such as voltage and temperature for predicting the characteristics of any other independent battery.

25 ENERGY STORAGE↗
DOE OSTI2023

Apparatus and method for determining state of change (SOC) and state of health (SOH) of electrical cells

Systems and methods for prediction of state of charge (SOH), state of health (SOC) and other characteristics of batteries using acoustic signals, includes determining acoustic data at two or more states of charge and determining a reduced acoustic data set representative of the acoustic data at the two or more states of charge. The reduced acoustic data set includes time of flight (TOF) shift, total signal amplitude, or other data points related to the states of charge. Machine learning models use at least the reduced acoustic dataset in conjunction with non-acoustic data such as voltage and temperature for predicting the characteristics of any other independent battery.

Steingart, Daniel Artemis↗
DOE OSTI2019

Bond Wire Damage Detection and SOH Estimation of a Dual Pack IGBT Power Module using Active Power Cycling and Reflectometry

High thermal and electrical stress, over a period of time tends to cause deterioration of the health of power electronic switches. A huge fraction of research efforts involves the study of power electronic device reliability and development of novel techniques with higher accuracy in health estimation of such devices. Until today, no other existing techniques can determine the number of lifted bond wires and their locations in a live IGBT module. Through this paper, a new method for online condition monitoring of IGBTs and IGBT modules using SSTDR has been proposed. Unlike traditional methods, this research work concentrates at the gate terminals of the device instead of looking at the collector side. In addition, the RL-equivalent circuit to represent a bond wire has been developed for the DUT and simulated in CST Studio Suite to measure the reflection amplitudes. Both the experimental and simulation results have been compared. These results prove that a single measurement is sufficient to predict the failure of the device instead of looking at the traditional precursor parameter. With only two sets of measurements, it is possible to locate the aged device inside a module and detect the number of bond wire lift offs associated to that device.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2025

SOH v1.0.0-alpha

Explore the source record for details and available documents.

58 GEOSCIENCES↗
DOE OSTI2025

SOH v1.0.0-beta

Explore the source record for details and available documents.

58 GEOSCIENCES↗
DOE OSTI2025

Impedance-Based State-of-Health Estimation for Lithium-Ion Battery Management Systems

This paper discusses an impedance-based state-of-health (SOH) estimation for lithium-ion battery management systems. Features obtained from the Nyquist plot of the electrochemical impedance spectroscopy (EIS) are utilized to estimate the SOH. Two commercial lithium-ion batteries were aged (cycled) in the laboratory to collect aging and impedance data. An algorithm for SOH estimation is presented and evaluated. The performance evaluation results show that the SOH features extracted from the Nyquist plot can be utilized for SOH estimation. Estimated SOH values can be then utilized in various battery management systems (BMS) functions such as for calibrating the available capacity, adjusting charging/discharging strategies, and protection.

Al-Smadi, Mohammad↗
DOE OSTI2025

Data-Driven State of Health Estimation for Second-Life Batteries Using Interpolated Synthetic Data and Feature Selection

Accurate estimation of the State of Health (SOH) for second-life batteries (SLBs) is crucial given their increasing use in energy storage applications. Precise SOH prediction is essential for safe operation and robust battery management systems. A major challenge is the limited availability of datasets for building reliable degradation models. To address this, synthetic data generation through linear interpolation is performed to extend the available data, making it more representative of real-world battery operating conditions. By analyzing feature correlation with SOH, the most relevant features are selected for the model. The proposed approach employs a convolutional neural network (CNN) model trained on this interpolated, feature-selected dataset, using time series data of voltage, temperature, and current over a cycle. By focusing on highly correlated features, the model achieves over 95% accuracy, with mean absolute error and root mean squared error up to 2.27% and 2.64%, respectively, in SOH estimation for two battery datasets tested. These results highlight the potential of combining synthetic data generation and feature selection to enhance SOH predictions, showcasing the superior performance of the proposed CNN model for both new batteries and SLBs.

feature selection↗
DOE OSTI2024

Designer Fluorescent Redoxmer Self‐Reports Side Reactions in Nonaqueous Redox Flow Batteries

The state of health (SOH) is a critical measure for evaluating and predicting performance of redox flow batteries (RFBs). However, diagnosing SOH of RFBs is often challenging due to the overwhelming complexity of the electrolytes and associated electrochemical reactions. Designing active molecules or redoxmers that can autonomously exhibit property changes upon specific stimuli may provide a viable way for early diagnosis of SOH. Herein, a dimerized redoxmer, DGL-N-CH 3 , was designed and synthesized by linking blue-green fluorescent monomers through a diglycolamide linker. While DGL-N-CH 3 still maintains similar electrochemical behavior and strong fluorescence, we observe a unique side reaction when cycling DGL-N-CH 3 in H-cells, which leads to a side product, NHCH 3 -BzNSN via linker cleavage. Interestingly, NHCH 3 -BzNSN also emits fluorescence but at a longer wavelength. By taking advantage of this unique fluorescent change that corresponds to the growth of NHCH 3 -BzNSN, we successfully established the capacity decay of DGL-N-CH 3 H-cell cycling, exemplifying a proof-of-concept self-reporting redoxmer design towards in situ SOH monitoring.

25 ENERGY STORAGE↗