Search NASA⌕ Search

SEARCH · Search NASA

Results for “cell balancing”

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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Design, Optimization, and Validation of GaN-Based DAB Converter for Active Cell Balancing in BTMS Applications

This paper focuses on the design of a bidirectional dual active bridge (DAB) DC/DC converter that utilizes Gallium Nitride (GaN) switches as active components. In the existing literature, MOSFET-based DAB for active cell balancing is available, but GaN-based DAB converter for active cell balancing is still new. The proposed modular isolated GaN-based DAB converter is designed as an individual module of active cell balancing for behind-the-meter storage (BTMS) applications, targeting high-power charging stations. Modular isolated converters are connected to each cell (low voltage bus), and each cell is connected in series to build up a battery module. According to the reference current command of supervisory control, each DAB converter can transfer power back and forth through the high voltage (HV) bus to balance the State of Charge (SoC) between the cells. Each module DAB converter is designed at a 50 W power rating. Switch power and transformer losses are analyzed for different switching frequencies, showing the optimum switching frequency for minimum losses. Furthermore, the procedure to select the required gate driver and the PCB layout optimization are discussed. Finally, the DAB performance analysis of GaN-based DAB and Si-based DAB is provided for a battery module operating with a LiFeMnPO4 prismatic cell with 3.2V 20Ah rated values.

active cell balancing↗
DOE OSTI2023

Design, Optimization, and Validation of GaN-Based DAB Converter for Active Cell Balancing in BTMS Applications: Preprint

This paper focuses on the design of a bidirectional dual active bridge (DAB) DC/DC converter that utilizes Gallium Nitride (GaN) switches as active components. In the existing literature, MOSFET-based DAB for active cell balancing is available, but GaN-based DAB converter for active cell balancing is still new. The proposed modular isolated GaN-based DAB converter is designed as an individual module of active cell balancing for behind-the-meter storage (BTMS) applications, targeting high-power charging stations. Modular isolated converters are connected to each cell (low voltage bus), and each cell is connected in series to build up a battery module. According to the reference current command of supervisory control, each DAB converter can transfer power back and forth through the high voltage (HV) bus to balance the State of Charge (SoC) between the cells. Each module DAB converter is designed at a 50W power rating. Switch power and transformer losses are analyzed for different switching frequencies, showing the optimum switching frequency for minimum losses. Furthermore, the procedure to select the required gate driver and the PCB layout optimization are discussed. Finally, the DAB performance analysis of GaNbased DAB and Si-based DAB is provided for a battery module operating with a LiFeMnPO4 prismatic cell with 3.2V 20Ah rated values.

active cell balancing↗
DOE OSTI2026

Battery Life Prediction Using Reduced-Order Physics Models and Machine Learning (CRADA Final Report)

Phase 1 (Original CRADA, plus no-cost extension modifications #1-3, 6/1/2017 to 3/13/2021): The Australian Department of Defence (AUDoD) is performing accelerated aging tests of Li-ion batteries to benchmark their reliability and degradation characteristics. Using its previously developed battery lifetime predictive model framework, the National Laboratory of the Rockies (NLR) will develop analytical models based the AUDoD data to predict lifetime of the multiple Li-ion battery chemistries under real-world use scenarios of interest to AUDoD. The NLR model is based on physical degradation mechanisms encountered by Li-ion batteries and has been previously validated. Phase 2 (CRADA modification #4, plus no-cost extension modification #5, 2/22/2021 to 3/30/2025): Train and support Australian Department of Defence personnel to use NLR software for model-based estimation of Li-ion battery lifetime using accelerated battery aging data collected by the Australian Department of Defence. Under separate DOE funding from 2019 to 2021, NLR enhanced its battery life-prediction software using machine learning algorithms to automate portions of the model-fitting process, requiring significantly less labor and expert judgment and also adding uncertainty quantification, increasing statistical rigor. Under Phase 2, NLR will customize NLR Software and provide it to AuDoD. NLR will enhance its NLR Model to capture aging modes of AuDoD's multi-cell modules, including cell-balancing effects. NLR will develop example single-cell and multi-cell models based on one AuDoD battery aging dataset. NLR will train AuDoD personnel on NLR Software. By the conclusion of the project, NLR will have provided AuDoD the training materials, a user manual and software needed to perform their own analysis of additional and/or future battery aging datasets.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

Optimal Power Management for Large-Scale Battery Energy Storage Systems via Bayesian Inference

Large-scale battery energy storage systems (BESS) have found ever-increasing use across industry and society to accelerate clean energy transition and improve energy supply reliability and resilience. However, their optimal power management poses significant challenges: the underlying high-dimensional nonlinear nonconvex optimization lacks computational tractability in real-world implementation, and the uncertainty of the exogenous power demand makes exact optimization difficult. This paper presents a new solution framework to address these bottlenecks. The solution pivots on introducing power-sharing ratios to specify each cell’s power quota from the output power demand. To find the optimal power-sharing ratios, we formulate a nonlinear model predictive control (NMPC) problem to achieve power-loss-minimizing BESS operation while complying with safety, cell balancing, and power supply-demand constraints. We then propose a parameterized control policy for the power-sharing ratios, which utilizes only three parameters, to reduce the computational demand in solving the NMPC problem. This policy parameterization allows us to translate the NMPC problem into a Bayesian inference problem for the sake of 1) computational tractability, and 2) overcoming the nonconvexity of the optimization problem. We leverage the ensemble Kalman inversion technique to solve the parameter estimation problem. Concurrently, a low-level control loop is developed to seamlessly integrate our proposed approach with the BESS to ensure practical implementation. This low-level controller receives the optimal power-sharing ratios, generates output power references for the cells, and maintains a balance between power supply and demand despite uncertainty in output power. We conduct extensive simulations and experiments on a 20-cell prototype to validate the proposed approach.

Battery energy storage systems (BESSs)↗
DOE OSTI2022

High Energy Density Aqueous Flow Battery Utilizing Extremely Stable, Branching-Induced High-Solubility Anthraquinone near Neutral pH

An anthraquinone featuring a chiral carboxylate-capped methyl-branched side chain with an ether linkage, 2,2'-((9,10-dioxo-9,10-dihydroanthracene-2,6-diyl)bis(oxy))dipropionic acid (2,6-D2PEAQ), was synthesized and evaluated for use in aqueous redox flow batteries. It was found to have an extraordinary solubility of 2 M (4 M electrons), corresponding to a theoretical volumetric capacity of 107.2 Ah/L for the negative electrolyte, which is 10 times that of its unbranched counterpart. The 2,6-D2PEAQ molecule demonstrated stability against thermal 2 decomposition and was extremely stable under cell cycling conditions. Here, a capacity fade rate of 0.02% day over 14 days was demonstrated in a 1.1 M 2,6-D2PEAQ nearly capacity-balanced cell when paired with a ferro-/ferricyanide posolyte at pH 7. Compared to other aqueous redoxactive organic molecules, its demonstrated fade rate is lower than that of any molecule with a demonstrated volumetric capacity of ≥55 Ah/L, and its volumetric capacity is greater than that of any molecule with a demonstrated fade rate of ≤0.5%/day.

25 ENERGY STORAGE↗
DOE OSTI2021

The mystery and promise of multivalent metal-ion batteries

Despite mounting interest and extensive research efforts in developing multivalent (MV) metal-ion battery chemistries (Zn 2+ , Mg 2+ , Ca 2+ , Al 3+ , etc.), the commercial prospects for these energy storage systems are still obfuscated by fundamental scientific questions and engineering challenges. In particular, the charge storage mechanism(s) of the positive electrode and the implications on achievable electrode and full cell-level performance remain intensely debated topics in the field. Here, in this minireview, we highlight some of the recent progress and uncertainty in elucidating capacity contributions and accompanying mechanisms for different charge carriers (H + , MV-ion, structural ion, anion, and charged solvates/complexes). We then present cell balancing and increasing metal anode utilization as major remaining challenges and propose design strategies and future directions for realizing practical MV metal-ion batteries.

25 ENERGY STORAGE↗
DOE OSTI2020

An Efficient Electrochemical Tanks-in-Series Model for Lithium Sulfur Batteries

This article applies and efficiently implements the Tanks-in-Series methodology ( J. Electrochem. Soc. , 167 , 013534 (2020)) to generate a computationally efficient electrochemical model for Lithium-Sulfur batteries. The original Tank model approach for Lithium-ion batteries is modified to account for porosity changes with time. In addition, an exponential scaling method is introduced that enables efficient simulation of the model equations to address the wide range of time constants present for different reactions in the Lithium-Sulfur system. The Tank Model achieves acceptable voltage error even for transport-limited discharged conditions. Predictions of internal electrochemical variables are examined, and electrochemical implications of the approximations discussed. This suggests significant potential for real-time applications such as optimal charging, cell-balancing, and estimation, and represents a step forward in efforts to incorporate detailed electrochemical models in advanced Battery Management Systems for Lithium-Sulfur batteries.

Parke, Caitlin D. (ORCID:0000000177324044)↗
DOE OSTI2024

Balancing Flexible Side Chains on 2D Conjugated Acceptors Enables High‐Performance Organic Solar Cell

Abstract Balancing the rigid backbones and flexible side chains of light‐harvesting materials is crucially important to reach optimized intermolecular packing, micromorphology, and thus photovoltaic performance of organic solar cells (OSCs). Herein, based on a distinctive CH‐series acceptor platform with 2D conjugation extended backbones, a series of nonfullerene acceptors (CH‐6F‐Cn) are synthesized by delicately tuning the lengths of flexible side chains fromn‐octyl ton‐amyl. A systemic investigation has revealed that the variation of the side chain's length can not only modulate intermolecular packing modes and crystallinity but also dramatically improve the micromorphology of the active layer and eventual photovoltaic parameters of OSCs. Consequently, the highest PCE of 18.73% can be achieved by OSCs employingD18:PM6:CH‐6F‐C8as light‐harvesting materials.

Chemistry↗
DOE OSTI2021

An oscillatory network controlling self-renewal of skeletal muscle stem cells

The balance between proliferation and differentiation of muscle stem cells is tightly controlled, ensuring the maintenance of a cellular pool needed for muscle growth and repair. Muscle stem cells can proliferate, they can generate differentiating cells, or they self-renew to produce new stem cells. Notch signaling plays a crucial role in this process. Recent studies revealed that expression of the Notch effector HES1 oscillates in activated muscle stem cells. The oscillatory expression of HES1 periodically represses transcription from the genes encoding the myogenic transcription factor MYOD and the Notch ligand DLL1, thereby driving MYOD and DLL1 oscillations. This oscillatory network allows muscle progenitor cells and activated muscle stem cells to remain in a proliferative and ‘undecided’ state, in which they can either differentiate or self-renew. When HES1 is downregulated, MYOD oscillations become unstable and are replaced by sustained expression, which drives the cells into terminal differentiation. During development and regeneration, proliferating stem cells contact each other and the stability of the oscillatory expression depends on regular DLL1 inputs provided by neighboring cells. In such communities of cells that receive and provide Notch signals, the appropriate timing of DLL1 inputs is important, as sustained DLL1 cannot replace oscillatory DLL1. Thus, in cell communities, DLL1 oscillations ensure the appropriate balance between self-renewal and differentiation. In summary, oscillations in myogenic cells are an important example of dynamic gene expression determining cell fate.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Cyanobacterial circadian regulation enhances bioproduction under subjective nighttime through rewiring of carbon partitioning dynamics, redox balance orchestration, and cell cycle modulation

Abstract Background The industrial feasibility of photosynthetic bioproduction using cyanobacterial platforms remains challenging due to insufficient yields, particularly due to competition between product formation and cellular carbon demands across different temporal phases of growth. This study investigates how circadian clock regulation impacts carbon partitioning between storage, growth, and product synthesis in Synechococcus elongatus PCC 7942, and provides insights that suggest potential strategies for enhanced bioproduction. Results After entrainment to light-dark cycles, PCC 7942 cultures transitioned to constant light revealed distinct temporal patterns in sucrose production, exhibiting three-fold higher productivity during subjective night compared to subjective day despite moderate down-regulation of genes from the photosynthetic apparatus. This enhanced productivity coincided with reduced glycogen accumulation and halted cell division at subjective night time, suggesting temporal separation of competing processes. Transcriptome analysis revealed coordinated circadian clock-driven adjustment of the cell cycle and rewiring of energy and carbon metabolism, with over 300 genes showing differential expression across four time points. The subjective night was characterized by altered expression of cell division-related genes and reduced expression of genes involved in glycogen synthesis, while showing upregulation of glycogen degradation pathways, alternative electron flow components, the pentose phosphate pathway, and oxidative decarboxylation of pyruvate. These molecular changes created favorable conditions for product formation through enhanced availability of major sucrose precursors (glucose-1-phosphate and fructose-6-phosphate) and maintained redox balance through multiple mechanisms. Conclusions Our analysis of circadian regulatory rewiring of carbon metabolism and redox balancing suggests two potential approaches that could be developed for improving cyanobacterial bioproduction: leveraging natural circadian rhythms for optimizing cultivation conditions and timing of pathway induction, and engineering strains that mimic circadian-driven metabolic shifts through controlled carbon flux redistribution and redox rebalancing. While these strategies remain to be tested, they could theoretically improve the efficiency of photosynthetic bioproduction by enabling better temporal separation between cell growth, carbon storage accumulation, and product synthesis phases.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

High Efficiency Inverted GaAs and GaInP/GaAs Solar Cells With Strain-Balanced GaInAs/GaAsP Quantum Wells

High-efficiency solar cells are essential for high-density terrestrial applications, as well as space and potentially vehicle applications. The optimum bandgap for the terrestrial spectrum lies beyond the absorption range of a traditional dual junction GaInP/GaAs cell, with the bottom GaAs cell having higher bandgap energy than necessary. Lower energy bandgaps can be achieved with multiple quantum wells (QWs), but such a pathway requires advanced management of the epitaxial growth conditions in order to be useful. Strain-balanced GaAsP/GaInAs QWs are incorporated into a single junction GaAs solar cell and a dual junction GaInP/GaAs solar cell, leading to 27.2% efficiency in the single junction device and a one-sun record 32.9% efficiency in the tandem device. Good carrier collection and low non-radiative recombination are observed in the cells with up to 80 QWs. The GaAs cells employ a rear-heterojunction architecture to boost the open-circuit voltage to over 1.04 V in the quantum well device, despite the large number of QWs.

14 SOLAR ENERGY↗
DOE OSTI2024

The RNA-binding protein Modulo promotes neural stem cell maintenance in Drosophila

A small population of stem cells in the developing Drosophila central nervous system generates the large number of different cell types that make up the adult brain. To achieve this, these neural stem cells (neuroblasts, NBs) divide asymmetrically to produce non-identical daughter cells. The balance between stem cell self-renewal and neural differentiation is regulated by various cellular machinery, including transcription factors, chromatin remodelers, and RNA-binding proteins. The list of these components remains incomplete, and the mechanisms regulating their function are not fully understood, however. Here, we identify a role for the RNA-binding protein Modulo (Mod; nucleolin in humans) in NB maintenance. We employ transcriptomic analyses to identify RNA targets of Mod and assess changes in global gene expression following its knockdown, results of which suggest a link with notable proneural genes and those essential for neurogenesis. Mod is expressed in larval brains and its loss leads to a significant decrease in the number of central brain NBs. Stem cells that remain lack expression of key NB identity factors and exhibit cell proliferation defects. Mechanistically, our analysis suggests these deficiencies arise at least in part from altered cell cycle progression, with a proportion of NBs arresting prior to mitosis. Overall, our data show that Mod function is essential for neural stem cell maintenance during neurogenesis.

Parra, Amalia S.↗
DOE OSTI2020

Rear Heterojunction GaAs Solar Cells With Strain-Balanced GaInAs/GaAsP Quantum Wells

We have fabricated GaAs rear-heterojunction solar cells with strain-balanced GaInAs/GaAsP multiple quantum wells to increase the range of absorption. The overall device architecture includes a thick n-type GaAs emitter layer, followed by undoped multiple quantum wells (QWs) and then a heterojunction with a higher bandgap GaInP p-type base. The QWs shift the band edge to from ~870 nm to 930 nm and results in >2 mA/cm 2 collection below the GaAs bandgap. Due to the heterojunction, the cell demonstrated a high open-circuit voltage of Voc=1.03V and an efficiency of 26.4% at 1000 W/m 2.

14 SOLAR ENERGY↗
DOE OSTI2022

Record Efficiency Multijunction Solar Cells with Strain-Balanced Quantum Well Superlattices

The absorption edge of a GaAs solar cell can be tuned to longer wavelengths with strain-balanced quantum wells, as an alternative to metamorphic epitaxy. For one-sun photovoltaic applications, an optimum bandgap for both single and multijunction solar cells is near the broad water absorption peak at 930 nm and can be accessed by a Ga0.9In0.1As alloy. This alloy is strained with respect to the GaAs substrate, and so only very thin ~10 nm layers can be grown without relaxation via dislocation formation. However, solar cells require significant thickness for complete absorption of the incoming solar spectrum, requiring many such layers. We have designed quantum well superlattices with up to 300 sets of carefully tuned strain-balanced GaInAs/GaAsP pairs by limiting composition modulation and material degradation. We have incorporated quantum well solar cells into multijunction devices to achieve record 32.9% two-junction and 39.5% three-junction solar cells under standard one-sun illumination. We will describe the physics of the quantum well devices and the growth conditions that lead to sharp interfaces and good material quality, as well as the characterization of the efficiencies.

GaAs↗
DOE OSTI2021

32.9% Efficient Tandem Solar Cell with Strain-Balanced GaInAs/GaAsP Quantum Wells

We demonstrate single and dual junction solar cells with up to 80 strain-balanced GaInAs/GaAsP quantum wells (QWs), in order to extend the range of photon absorption. The net average stress in the quantum well layers is =12 MPa, despite coherent stresses in the individual layers that are 35-70 times greater. The GaAs-QW cell has a certified efficiency of (27.2 ± 0.2)% at one-sun; the GaInP/GaAs-QW tandem has a certified record one-sun efficiency of (32.9 ± 0.5)%. We will discuss the architecture of the devices, the physics behind the high performance, and pathways to further improvement.

III-V↗
DOE OSTI2021

32.9% Efficient Tandem Solar Cell with Strain-Balanced GaInAs/GaAsP Quantum Wells: Preprint

We demonstrate single and dual junction solar cells with up to 80 strain-balanced GaInAs/GaAsP quantum wells (QWs), in order to extend the range of photon absorption. The net average stress in the quantum well layers is =12 MPa, despite coherent stresses in the individual layers that are 35-70 times greater. The GaAs-QW cell has a certified efficiency of (27.2 ± 0.2)% at one-sun; the GaInP/GaAs-QW tandem has a certified record one-sun efficiency of (32.9 ± 0.5)%. We will discuss the architecture of the devices, the physics behind the high performance, and pathways to further improvement.

GaAsP↗
DOE OSTI2025

Nanolipoprotein particle (NLP) vaccine confers protection against Yersinia pestis aerosol challenge in a BALB/c mouse model

Introduction: Yersinia pestis is the etiological agent of plague, a disease that remains a concern as demonstrated by recent outbreaks in Madagascar. Infection with Y. pestis results in a rapidly progressing illness that can only be successfully treated with antibiotics given shortly after symptom onset. Live attenuated or whole cell inactivated vaccines confer protection against bubonic plague, but pneumonic plague has been more difficult to prevent. Novel effective subunit vaccine formulations may circumvent some of these shortfalls. Here, we compare the immunogenicity generated by an advanced subunit vaccine (F1V fusion protein) and a nanolipoprotein particle (NLP)-based vaccine. Methods: The NLP, a high-density lipoprotein mimetic, provides a nanoscale delivery platform for recombinant Y. pestis antigens LcrV (V) and F1. BALB/c mice were immunized via subcutaneous injection twice, three or four weeks apart. Four weeks later, splenocytes and sera were collected for immune profiling, and mice were challenged with aerosolized Y. pestis CO92. Results: Both formulations induced a strong IgG response against the F1 and V proteins, along with a robust memory B cell response and a balanced cell-mediated immune response as evidenced by both Th1- and Th2-related cytokines. The NLP-based vaccine induced a stronger cytokine response against F1, V, and F1V proteins relative to the F1V vaccine. As with F1V, the inclusion of Alhydrogel (Alu) in NLP vaccine formulations was critical for enhanced immunogenicity and protective efficacy. Mice that received two doses of F1:V:NLP + Alu and CpG were completely protected from a challenge with approximately eight median lethal doses of aerosolized Y. pestis CO92 and this protection confirmed the well-documented synergy between the F1 and V antigens in context of pneumonic plague. The NLPs have defined regions of polarity that facilitates the incorporation of a wide range of adjuvants and antigens with distinct physicochemical properties and are an excellent candidate platform for the development of multi-antigen vaccines.

F1↗