Search NASA⌕ Search

SEARCH · Search NASA

Results for “battery health”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Feedback-Based Fault-Tolerant and Health-Adaptive Optimal Charging of Batteries

The key technology barriers that hinder the growth of Electric Vehicles (EVs) are long charging time, the shorter life-time of EV batteries, and battery safety. Specifically, EV charging protocols have significant effects on battery lifetime and safety. If not charged properly, the battery could end up with shorter life, and more importantly, improper charging can cause battery faults leading to catastrophic failures. To overcome these barriers, we propose a closed-loop feedback based approach, that enables real-time optimal fast charging protocol adaptation to battery health and possess active diagnostic capabilities in the sense that, during charging, it detects real-time faults and takes corrective action to mitigate such fault effects. We utilize battery electrical-thermal model, explicit battery capacity and power fade aging models, and thermal fault model to capture battery behavior. In conjunction with the models, we adopt linear quadratic optimal control techniques to realize the feedback-based control algorithm. Simulation studies are presented to illustrate the effectiveness of the proposed scheme.

batteries↗
NASA NTRSDate not supplied

Sensor-based Prognostics for Safe Batteries for Electric Aircraft

With a new start awarded under CAS for Fiscal Year 2020, the Sensor-based Prognostics to Avoid Runaway Reactions and Catastrophic Ignition project, or SPARRCI, aims to eliminate safety risks of lithium-ion and next-generation lithium-based batteries. SPARRCI is taking a deeper dive into the inner workings of battery cells by introducing miniature multifunctional sensors within the cells to detect early signs of failure. Combined with nondestructive evaluation techniques to gather complementary battery health data, battery data will be monitored in real-time via prognostics which will ultimately be able to identify when the cell is exhibiting abnormal behavior bordering on failure, with the ability to provide early warning such that the battery may be taken offline prior to catastrophic failure events.

Brianne Demattia↗
DOE OSTI2024

Battery State of Health Estimator: Cooperative Research and Development Final Report

NREL has developed a software tool to enable Renewance to estimate the degradation of batteries from basic information such as the type of battery and the application of that battery during its first life, so that used batteries may be evaluated for potential repurposing at low cost. This software tool utilizes NREL's BLAST-Lite battery degradation modeling code, which was updated with additional models for commercially produced lithium-ion batteries as a part of this CRADA. The software tool enables users to input details such as battery type and application so that lifetime estimates can be made without any programming or expert battery knowledge. The application input loads in saved values for parameters such as cycles per year, depth-of-discharge, and other battery operating parameters from a file defined by Renewance. These parameters may be modified to refine simulations for specific batteries. The software tool also incorporates a degradation model optimization tool, whereby existing battery degradation models may be tuned according to measured battery health. This ensures that new models still predict degradation behaviors expected from a certain battery chemistry, but with the overall degradation rate tuned to a specific battery make and model. The new model can then be saved for estimating the degradation of other similar batteries. An additional task was planned to utilize machine-learning to enable battery health diagnosis from rapid EIS measurements to accelerate the screening of used batteries. This task was not completed due to lack of available data for training a machine-learning model. CRADA benefit to DOE, Participant, and US Taxpayer: Further development of open-source software tool BLAST-Lite for predicting the lifetime of commercially produced Lithium-ion batteries (NREL SWR-22-69).

25 ENERGY STORAGE↗
NASA NTRSDate not supplied

Enabling in-time Prognostics with Surrogate Modeling through Physics-enhanced Dynamic Mode Decomposition Method

Computational models provide essential quantitative tools for assessing and predicting the health and performance of physical systems. However, high-fidelity models are rarely used in real-time operations or large optimization loops, due to their time-intensive nature. A common approach to improving computational efficiency of prognosis is to employ surrogate models. Such models can significantly decrease computation time for some accuracy loss. In this context, use of Dynamic Mode Decomposition (DMD) is proposed to generate surrogate models for lithium-ion (Li-ion) battery discharge. DMD has been suggested and used successfully in the area of fluid dynamics for over a decade, but it has not been applied to the PHM domain, where far-ahead prediction of nonlinear behavior is crucial to propagate faults or predict Remaining Useful Life (RUL). For Li-ion battery health management, the standard application of DMD using only the observable quantities of interest was unable to capture the nonlinear discharge of batteries exhibited in lab testing. The Koopman theory, however, provides a mechanism to tradeoff low dimensional nonlinear models with high-dimensional linear ones in a DMD framework, by augmenting nonlinear state variables into the system representation. In this way, DMD allows for configurable simulation accuracy dependent on the dimensionality of the Koopman operator. For battery health management, we augmented the observable variables with the hidden states of a higher-fidelity physics model to build the DMD surrogate. In comparison to a high-fidelity model, the surrogate improved computational efficiency with only a minimal loss of accuracy, and enabled long-term prognostics horizons. A generalized method for this was implemented in the prog models python package.

prognostics and health management↗
NASA NTRSDate not supplied

Flight Deck Design of a Hybrid Turbine/Electric Passenger Aircraft

NASA is exploring the development of a 180-passenger subsonic single engine aft turbine aircraft, The aft turbine provides electric power in a hybrid design to wing mounted electric engines, creating a highly efficient, high-bypass-ratio fan equivalent. The SUbsonic Single Aft eNgine (SUSAN) aircraft is being developed as a sustainable subsonic regional aircraft that seeks to reduce emission levels by 50% in the next few decades. Pilot-in-the-loop studies were conducted at the NASA Langley Research Center in Hampton, Virginia, to explore the flight deck design for the hybrid electric aircraft. Following modern trends in commercial aircraft flight decks with full time augmented controls and a quiet and dark philosophy, single throttle and simplified engine displays were developed for the SUSAN aircraft. The aircraft includes a single aft mounted turbine engine and 16 wing mounted electric fans. The final design was developed from feedback received during an earlier pilot-in-the-loop study where one, two, and three throttles were tested in standard airline operations, including various failures of the turbine and electric engines. Current flight deck designs normally provide control inceptors for each propulsion engine and an engine display for all primary aircraft engine parameters. With full time augmentation expected, a single throttle control with autothrottle always engaged, even during failures, is desired. Augmentation of flight controls using distributed thrust also requires full time control of the electric engines using automation. Additionally, electric engine thrust is augmented during climb based on battery state of charge. Thrust augmentation changes faster than human reaction time and therefore requires full-time automation. A pilot-in-the-loop study was conducted at the NASA Langley Research Center in Hampton, Virginia, to test the final design of the single throttle with simplified engine displays. Fourteen airline pilots evaluated the single throttle and engine display concept. Electric engine failures included one, four symmetric, and eight non-symmetric electric engine failures. The turbine engine was evaluated for complete and partial failure during critical phases of flight to include takeoff as well as enroute. Unexpected go-arounds increase workload and require significant throttle manipulation. Go-arounds were included to ensure the single throttle was usable for all phases of flight. Failures during takeoff required a return to the departure field and failures enroute required a diversion except for one and four electric engine failures as these failures did not affect aircraft flyability or range. There are currently no Part 25 aircraft certified with hybrid systems or electric engines with batteries as emergency propulsion. For turbine engine failures in the SUSAN aircraft design, range is limited to 30 minutes at full power. Battery state of charge and battery health displays were developed and tested for usability and to determine how well they supported pilot decisions for alternate airports during emergency diversions. Novel displays using shape and color were developed to provide immediate feedback when state of charge became critical. This paper details the pilot study including pilot feedback supporting the potential for increased automation and a single throttle control. Detailed recommendations are provided for a novel single throttle control and additional pilot controls to support selection of engines during start, shutdown, and engine troubleshooting procedures. This design deviates significantly from current practice of providing throttles for each propulsion engine. Engine display recommendations are provided based on pilot feedback during a guided post-evaluation interview. Battery state of charge and battery health display recommendations were collected from all airline crews. The simplified engine displays design was rated excellent as measured with a usability scale. Quantitative metrics include airspeed tracking, time to complete checklists, time to make diversion decisions and the quality of the diversion decision. Recommendations for future studies are documented with supporting research and current observations about upcoming flight deck certifications.

autothrottle↗
NASA NTRSDate not supplied

Flight Deck Design of a Hybrid Turbine/Electric Passenger Aircraft

NASA is exploring the development of a 180-passenger subsonic single engine aft turbine aircraft, The aft turbine provides electric power in a hybrid design to wing mounted electric engines, creating a highly efficient, high-bypass-ratio fan equivalent. The SUbsonic Single Aft eNgine (SUSAN) aircraft is being developed as a sustainable subsonic regional aircraft that seeks to reduce emission levels by 50% in the next few decades. Pilot-in-the-loop studies were conducted at the NASA Langley Research Center in Hampton, Virginia, to explore the flight deck design for the hybrid electric aircraft. Following modern trends in commercial aircraft flight decks with full time augmented controls and a quiet and dark philosophy, single throttle and simplified engine displays were developed for the SUSAN aircraft. The aircraft includes a single aft mounted turbine engine and 16 wing mounted electric fans. The final design was developed from feedback received during an earlier pilot-in-the-loop study where one, two, and three throttles were tested in standard airline operations, including various failures of the turbine and electric engines. Current flight deck designs normally provide control inceptors for each propulsion engine and an engine display for all primary aircraft engine parameters. With full time augmentation expected, a single throttle control with autothrottle always engaged, even during failures, is desired. Augmentation of flight controls using distributed thrust also requires full time control of the electric engines using automation. Additionally, electric engine thrust is augmented during climb based on battery state of charge. Thrust augmentation changes faster than human reaction time and therefore requires full-time automation. A pilot-in-the-loop study was conducted at the NASA Langley Research Center in Hampton, Virginia, to test the final design of the single throttle with simplified engine displays. Fourteen airline pilots evaluated the single throttle and engine display concept. Electric engine failures included one, four symmetric, and eight non-symmetric electric engine failures. The turbine engine was evaluated for complete and partial failure during critical phases of flight to include takeoff as well as enroute. Unexpected go-arounds increase workload and require significant throttle manipulation. Go-arounds were included to ensure the single throttle was usable for all phases of flight. Failures during takeoff required a return to the departure field and failures enroute required a diversion except for one and four electric engine failures as these failures did not affect aircraft flyability or range. There are currently no Part 25 aircraft certified with hybrid systems or electric engines with batteries as emergency propulsion. For turbine engine failures in the SUSAN aircraft design, range is limited to 30 minutes at full power. Battery state of charge and battery health displays were developed and tested for usability and to determine how well they supported pilot decisions for alternate airports during emergency diversions. Novel displays using shape and color were developed to provide immediate feedback when state of charge became critical. This paper details the pilot study including pilot feedback supporting the potential for increased automation and a single throttle control. Detailed recommendations are provided for a novel single throttle control and additional pilot controls to support selection of engines during start, shutdown, and engine troubleshooting procedures. This design deviates significantly from current practice of providing throttles for each propulsion engine. Engine display recommendations are provided based on pilot feedback during a guided post-evaluation interview. Battery state of charge and battery health display recommendations were collected from all airline crews. The simplified engine displays design was rated excellent as measured with a usability scale. Quantitative metrics include airspeed tracking, time to complete checklists, time to make diversion decisions and the quality of the diversion decision. Recommendations for future studies are documented with supporting research and current observations about upcoming flight deck certifications.

autothrottle↗
DOE OSTI2022

Predicting battery capacity from impedance at varying temperature and state of charge using machine learning

Prediction of battery health from electrochemical impedance spectroscopy (EIS) data can enable rapid measurement of battery state in real-world applications without using additional sensors or time-consuming performance measurements. However, deconvoluting the effect of capacity, state of charge, and temperature on EIS response is complicated analytically. Here, various machine-learning models, such as linear, Gaussian process, random forest, and artificial neural network regression, are utilized to predict capacity from EIS using hundreds of capacity, direct current (DC) resistance, and EIS measurements recorded under varying conditions of health, temperature, and state of charge (SOC). Several feature extraction and selection methods from traditional electrochemical analysis and statistical modeling are explored using machine-learning pipelines. EIS data from just two frequencies can accurately predict capacity, and interrogation shows that the optimal set of frequencies is not usually intuitive. Best results are achieved with an ensemble model, which predicts battery capacity with a mean absolute error of 1.9% on data from unobserved cells.

25 ENERGY STORAGE↗
DOE OSTI2020

Fluorescence-Enabled Self-Reporting for Redox Flow Batteries

Monitoring battery health is challenging. Self-reporting enables the rapid health assessment of redox flow batteries (RFBs) and provides insight into degradation mechanisms of electrochemically active molecules (redoxmers). In this study, we introduce fluorescence as an orthogonal property to probe this chemistry in situ. An established anolyte redoxmer, 2,1,3-benzothiadiazole, is rendered an efficient blue-green fluorophore through minimalistic derivatization. One of the derivatives is electrochemically reversible with a long lifetime and cycling stability in the charged state. Spectroscopic measurements on this new redoxmer reveal strong effects of the electrolyte cation on the fluorescence that are useful for probing the solvent microenvironment. Using this probe, we demonstrate proof-of-concept in situ crossover sensing and characterize the effects of electrolyte composition on this crossover. In this way, real-time tracing of redoxmers in a flow cell is demonstrated, paving the way to include still more self-reporting functions into the redoxmers.

25 ENERGY STORAGE↗
NASA NTRS2022

Lithium-ion Battery Strain Gauge Monitoring and Depth of Discharge Estimation

Lithium-ion battery diagnostics and prognostics rely on measurements of electrical impedance, capacity, and voltage to infer the internal state of the battery. Mechanical changes to the cell structure represent an additional measure of the battery’s state because these changes are related to the overall battery health. As lithium-ion batteries are charged and discharged, lithium ions are inserted or removed from the anode and cathode, a process called intercalation and de-intercalation. As lithium ions intercalate and de-intercalate, they can cause changes to the lattice of the electrode particles, resulting in volumetric changes. These volumetric changes cause mechanical stresses and strains on the lithium-ion battery electrodes, and subsequently, the whole cell’s thickness varies as it is charged and discharged. This paper presents a study on the use of surface-mounted strain gauges for in-situ measurement of structural changes to lithium-ion batteries, along with a characterization of the unit-to-unit differences in strain response. A neural network modeling structure is then used to predict the battery’s depth of discharge under dynamic discharge conditions.

Lithium-ion batteries, depth of discharge, strain ↗
DOE OSTI2024

Catalyzing deep decarbonization with federated battery diagnosis and prognosis for better data management in energy storage systems

Industrial data analytics methods play a central role in improving energy storage performance and efficiency, impacting the future of electrified transportation and renewable electricity generation. However, significant challenges hinder the large-scale deployment of batteries. Conventional methods rely on centralized collection and processing of fleet-level data, leading to database size issues and privacy concerns due to potential data breaches. To enable scalable deployment of battery management systems, this article proposes a federated battery diagnosis and prognosis model, which distributes the processing of battery standard current-voltage-time-usage data in a privacy-preserving manner. Instead of transferring the raw data, this approach communicates only the locally processed parameters, thus reducing communication load and preserving data confidentiality. The federated model offers a paradigm shift in battery health management through privacy-preserving distributed methods for battery data processing and lifetime prediction, ensuring the reliable and sustainable deployment of lithium-ion batteries in a rapidly evolving world.

asset health management↗
DOE OSTI2023

BLAST-Lite (Battery Lifetime Analysis and Simulation Tool - Lite) [SWR-22-69] Related to: BLAST aka: BLAST-Py

Battery Lifetime Analysis and Simulation Toolsuite (BLAST) provides a library of battery lifetime and degradation models for various commercial lithium-ion batteries from recent years. Degradation models are identified from publicly available lab-based aging data using NREL's battery life model identification toolkit. The battery life models predicted the expected lifetime of batteries used in mobile or stationary applications as functions of their temperature and use (state-of-charge, depth-of-discharge, and charge/discharge rates). Model implementation is in both Python and MATLAB programming languages. The MATLAB code also provides example applications (stationary storage and EV), climate data, and simple thermal management options. For more information on battery health diagnostics, prediction, and optimization, see NREL's Battery Lifespan webpage.

Smith, Kandler↗
DOE OSTI2026

Machine Learning Digital Twin for Lithium Ion Battery State of Health Predictions

A digital twin system has been established to model the long term degradation of the state of health of lithium ion batteries. Two data streams result from the computational model of the system and from the physical experiment measurements. A machine learning pipeline has been developed at the nexus of these data streams. Leveraging the unique data sources in multiple transfer learning approaches has lead to the development of multiple cell specific machine learning digital twins. Our discussion on this first of its kind technology will cover challenges in data ingestion, scalability, architecture and orchestration, and research findings.

25 - ENERGY STORAGE↗
NASA NTRS1990

Development of nickel hydrogen battery expert system

The Hubble Telescope Battery Testbed employs the nickel-cadmium battery expert system (NICBES-2) which supports the evaluation of performances of Hubble Telescope spacecraft batteries and provides alarm diagnosis and action advice. NICBES-2 also provides a reasoning system along with a battery domain knowledge base to achieve this battery health management function. An effort to modify NICBES-2 to accommodate nickel-hydrogen battery environment in testbed is described.

Shiva, Sajjan G.↗
DOE OSTI2024

Multivariate prognosis of battery advanced state of health via transformers

De-risking energy storage investments necessary to meet CO 2 reduction targets requires a deep understanding of the connections between battery health, design, and use. The historical definition of the battery state of health (SOH) as the percentage of current versus initial capacity is inadequate for this purpose, motivating an expanded SOH consisting of an interrelated set of descriptors including capacity, energy, ionic and electronic impedances, open-circuit voltages, and microstructure metrics. In this work, we introduce deep transformer networks for the simultaneous prognosis of 28 battery SOH descriptors using two cycling datasets representing six lithium-ion cathode chemistries, multiple electrolyte/anode compositions, and different charge-discharge scenarios. The accuracy of these predictions for battery life (with an unprecedented mean absolute error of 19 cycles in predicting end of life for a lithium-iron-phosphate fast-charging dataset) illustrates the promise of deep learning toward providing enhanced understanding and control of battery health.

25 ENERGY STORAGE↗
DOE OSTI2021

Lithium-ion battery physics and statistics-based state of health model

A pseudo-2d model using COMSOL Multiphysics® software is developed to simulate performance and performance degradation of Li-ion batteries consisting of layered and olivine cathodes with graphite anode when subjected to peak shaving grid service. Multiple degradation pathways are considered, including solid electrolyte interphase (SEI) formation and breakdown at the anode, cathode dissolution and its synergistic effect on SEI formation at the anode. The model is validated by simulating commercial cylindrical cell performance. A global model is developed to simulate performance across all chemistries, along with individual chemistry models using global model parameters as initial values. There is good agreement between these models for various optimization parameters such as SEI equilibrium potential, cathode dissolution exchange current density, solvent diffusivity in the SEI and SEI ionic conductivity. To circumvent time constraints related to the COMSOL model, a 0d global model is developed which fits data well and provides more clarity on differences in cathode dissolution exchange current density. Again, good agreement for various optimization parameters is obtained among the COMSOL global & individual chemistry models and the 0-d model. The lessons learned from the physics-based model is used to develop a top down statistics-based model using current, voltage and anode volumetric change per mole lithium intercalated, along with their interactions as degradation predictors. This model predicts out of sample degradation for multiple grid services and electric vehicle drive cycle with high accuracy and provides the pathway to develop an efficient battery management system combining machine learning and findings from physics-based computationally intensive algorithms.

Crawford, Aladsair J.↗
NASA NTRS1991

Nickel hydrogen battery expert system

The Hubble Telescope Battery Testbed at MSFC uses the Nickel Cadmium (NiCd) Battery Expert System (NICBES-2) which supports the evaluation of performance of Hubble Telescope spacecraft batteries and provides alarm diagnosis and action advice. NICBES-2 provides a reasoning system along with a battery domain knowledge base to achieve this battery health management function. An effort is summarized which was used to modify NICBES-2 to accommodate Nickel Hydrogen (NiH2) battery environment now in MSFC testbed. The NICBES-2 is implemented on a Sun Microsystem and is written in SunOS C and Quintus Prolog. The system now operates in a multitasking environment. NICBES-2 spawns three processes: serial port process (SPP); data handler process (DHP); and the expert system process (ESP) in order to process the telemetry data and provide the status and action advice. NICBES-2 performs orbit data gathering, data evaluation, alarm diagnosis and action advice and status and history display functions. The adaptation of NICBES-2 to work with NiH2 battery environment required modification to all of the three component processes.

Shiva, Sajjan G.↗
NASA NTRS2024

Performance of Two Battery Prognostic Applications used by Two Octocopters for Safe Low Altitude Autonomous Flight Operations

This paper addresses the problem of building trust in online predictions of the remaining available flying time for two different electric Unmanned Aerial Vehicles (eUAVs) powered by lithium-ion-polymer batteries. Flight tests for various automation research missions for the two vehicles were monitored using two on-board battery health management applications to make predictions of the remaining flying time (RFT) for each eUAV and to predict the state of the battery. Playback of the voltage, current and temperature profiles of the battery discharge were used to assess the accuracy of the estimation of the voltage and the charge states of the models as well as the estimate of the RFT. The reference ground truth values were the observed landing time and the measured battery pack resting pack voltage 20 minutes after the flight. The predicted RFT, state of charge (SoC), and state of energy (SoE) were compared with the observed results. Noise values of one standard deviation from the mean values of the internal charge states of the battery model during a reference run were used to vary the states during simulation. One application used an equivalent circuit model of the electrical dynamics of the battery pack, and the other application used a reduced-order electrochemistry model. The variation of the model state components was compared to the variation in the estimate of the RFT and the variation in the SoE to estimate a confidence factor. Variation in the estimates caused by factors affecting the off-line laboratory parameter identification experiments is considered. Variation in the estimates due to environmental factors are discussed.

Assurance↗
DOE OSTI2026

Electrochemo-mechanics unlocks hidden dynamics of lithium plating under stacking pressure

Understanding and mitigating lithium plating remains one of the most pressing challenges in advancing the safety and longevity of lithium-ion batteries (LIBs). Here, we present a novel integrative framework that combines in-operando swelling force measurements with a physics-based electro-chemo-mechanical model to uncover previously inaccessible insights into lithium plating dynamics under mechanical constraints. Unlike existing approaches that focus primarily on electrochemical signatures, our approach captures the coupled mechanical responses of commercial pouch cells during cycling, revealing how mechanical constraints fundamentally alter degradation pathways. We demonstrate that the use of a moderate stacking pressure suppresses lithium plating and enhances lithium stripping. This mechanically driven structural effect, coupled with the electrochemical process, significantly extends the linear aging regime in LIBs. Intriguingly, intermittent capacity recovery events that were observed during the battery cycling suggest dynamic lithium reactivation, a phenomenon rarely captured in real-time. This study pioneers a stress-aware methodology for diagnosing and managing lithium plating, establishing a new paradigm for real-time battery health monitoring. The findings offer transformative implications for the design of durable, high-performance LIB systems, opening new avenues for intelligent control strategies in battery management systems.

Bhowmick, Amit↗