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Li, Jian

Publications and source records attributed to Li, Jian.

At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Transient Stability Enhancement via a Scalable RL Method with VSG Parameter Tuning

This paper presents a reinforcement learning (RL)-driven strategy to improve the transient stability of power systems via tuning parameters of multiple virtual synchronous generators (VSGs). We proposed a scalable method to support RL training convergence probability and speed, even when a large number of contingencies are considered. The proposed scalable RL framework first decomposes the large number of contingencies into multiple groups and then conducts parallel training for each group, decreasing the state space and complexity of each training. Additionally, we propose a contingency grouping algorithm to streamline the RL action space and facilitate the training. The proposed method is validated across various standard test systems.

Huang, Xiaoge↗
DOE OSTI2024

Improving the Freight Productivity of a Heavy-Duty, Battery Electric Truck by Intelligent Energy Management

This project aimed to enhance the range and reduce the operating costs of battery electric Class 8 trucks traveling over 250 miles daily. This was achieved through the development and implementation of an intelligent-Energy Management System (i-EMS) that leverages vehicle and operations data, physics-aware machine learning algorithms, and vehicle-to-cloud (V2C) connectivity. The project hypothesized that advanced machine learning algorithms and real-time data analytics could significantly improve the energy efficiency and range of these trucks. Key objectives included developing a physics-aware machine learning algorithm, implementing an i-EMS with V2C connectivity and physics-aware spatial data analytics (PSDA), and validating the system’s effectiveness with fleet partners HEB Companies and Murphy Logistics. Extensive data collection from vehicle operations, including vehicle characteristics, road conditions, and payload, was conducted. A machine learning algorithm was developed to predict energy consumption and enable proactive decision-making. The i-EMS was implemented on two Volvo VNR BEVs, with operators receiving charging and routing recommendations. Charging stations were installed at depot locations in Texas and Minnesota, with an additional on-route charger in Minnesota. Significant findings included a 14% range improvement for Murphy Logistics on a highway-driving eco-route and a 22% range improvement for HEB Companies on a city-driving eco-route. The i-EMS utilized rule-based methods and physics-based algorithms to predict and reduce energy consumption, with real-time monitoring and analysis through V2C connectivity enabling proactive decision-making. The project demonstrated the feasibility and economic viability of battery electric Class 8 trucks for long-haul operations, showcasing the potential of physics-aware machine learning in optimizing energy management. The successful implementation of the i-EMS in real-world scenarios validates its practical application and effectiveness, paving the way for the widespread adoption of battery electric vehicles in the freight transportation industry.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Improved Operational Stability of Blue Phosphorescent OLEDs by Functionalizing Phenyl‐Carbene Groups of Tetradentate Pt(II) Complexes

Abstract Stable and efficient deep‐blue organic light‐emitting diodes (OLEDs) are in high demand for display and lighting applications but are rarely reported due to their poor operational lifetimes. Herein, the study designs and synthesizes two novel N ‐heterocyclic carbene (NHC)‐based tetradentate Pt(II) complexes PtON5‐dtb and PtON5N‐dtb, and thoroughly investigate their electrochemical and photophysical properties. Functionalization of the NHC moieties can increase the metal‐to‐ligand charge transfer ( 1/3 MLCT) characters in their lowest triplet excited‐states, resulting in significantly shortened photoluminescent lifetimes and remarkably improved device performance. A deep blue OLED employing PtON5N‐dtb as an emitter exhibits a narrow spectral bandwidth with a full‐width at half maximum (FWHM) of 30 nm and a CIE y value of 0.17 and demonstrates a maximum external quantum efficiency (EQE) of 20.4% with a small efficiency roll‐off, which maintains a high EQE of 18.5% at 1000 cd m −2 . Moreover, the deep blue OLED also realizes a long‐measured operational lifetime LT 90 (time to 90% of the initial luminance) of 71 hours with an initial brightness of 1134 cd m −2 , corresponding to an estimated device lifetime LT 90 of 85 h at 1000 cd m −2 . This represented an eightfold lifetime improvement for PtON5N‐dtb‐based deep blue OLED compared to PtON7‐dtb in the same device setting.

Li, Guijie↗
DOE OSTI2023

Finite-Time Analysis of Whittle Index based Q-Learning for Restless Multi-Armed Bandits with Neural Network Function Approximation

Whittle index policy is a heuristic to the intractable restless multi-armed bandits (RMAB) problem. Although it is provably asymptotically optimal, finding Whittle indices remains difficult. In this paper, we present Neural-Q-Whittle, a Whittle index based Q-learning algorithm for RMAB with neural network function approximation, which is an example of nonlinear two-timescale stochastic approximation with Q-function values updated on a faster timescale and Whittle indices on a slower timescale. Despite the empirical success of deep Q-learning, the non-asymptotic convergence rate of Neural-Q-Whittle, which couples neural networks with two-timescale Q-learning largely remains unclear. This paper provides a finite-time analysis of Neural-Q-Whittle, where data are generated from a Markov chain, and Q-function is approximated by a ReLU neural network. Our analysis leverages a Lyapunov drift approach to capture the evolution of two coupled parameters, and the nonlinearity in value function approximation further requires us to characterize the approximation error. Combing these provide Neural-Q-Whittle with convergence rate, where is the number of iterations.

reinforcement learning, structured learning, conve↗
DOE OSTI2023

Volvo Pathway to Cost-Effective Commercialized Freight Efficiency (SuperTruck 2)

Volvo’s SuperTruck 2 (ST2) built on the success of the SuperTruck 1 (ST1) project, with the objective to research, develop, and demonstrate a Class 8 long-haul tractor-trailer concept truck designed with an integrated approach to maximize freight efficiency and achieve the following goals: - Greater than 100% improvement in vehicle freight efficiency (FE) on a ton-mile-per-gallon basis, with a stretch goal of 120% improvement relative to a 2009 baseline. - Greater than or equal to 55% engine brake thermal efficiency (BTE) demonstrated in an operational engine at a 65-mph cruise point on a dynamometer. - Develop technologies that are commercially cost effective in terms of a simple payback. This cooperative agreement with the US Department of Energy (DOE) had a total project cost of $40,000,000 with a 50% cost share, or $20,000,000, paid by the DOE. This project was awarded to Volvo Technology of America, LLC, who was the prime recipient and project lead. Metalsa, Michelin, Wabash, Bergstrom, University of Michigan, Oak Ridge National Laboratory, Johnson Matthey, Knight Transportation, Wegmans, and Motivo were partners / sub-recipients in this project.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

Metal-assisted delayed fluorescent emitters employing pyrido-pyrrolo-acridine and analogues

Metal-assisted delayed fluorescent (MADF) emitters including cyclic tetradentate platinum (II) and palladium (II) complexes employing 8H-pyrido[3′,2′:4,5]-pyrrolo[3,2,1-de]acridine and its analogues. These complexes provide improved color purity and enhanced operational stability and are suitable for luminescent labels, emitters for organic light emitting diodes (OLEDs), and lighting applications.

Li, Jian↗
DOE OSTI2023

Hole-blocking materials for organic light emitting diodes

An organic light emitting device including an emissive layer including a blue phosphorescent emitter, an electron transport layer, and a hole blocking layer between the emissive layer and the electron transport layer, wherein the hole blocking layer comprises a tetradentate palladium complex.

Li, Jian↗
DOE OSTI2023

High-rate, high-capacity electrochemical energy storage in hydrogen-bonded fused aromatics

Designing materials for electrochemical energy storage with short charging times and high charge capacities is a longstanding challenge. The fundamental difficulty lies in incorporating a high density of redox couples into a stable material that can efficiently conduct both ions and electrons. We report all-organic, fused aromatic materials that store up to 310 mAh g –1 and charge in as little as 33 s. This performance stems from abundant quinone/imine functionalities that decorate an extended aromatic backbone, act as redox-active sites, engage in hydrogen bonding, and enable a delocalized high-rate energy storage with stability upon cycling. Here, the extended conjugation and hydrogen-bonding-assisted bulk charge storage contrast with the surface-confined or hydration-dependent behavior of traditional inorganic electrodes.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Display and lighting devices comprising phosphorescent excimers with preferred molecular orientation as monochromatic emitters

An organic light emitting diode having a substrate, a first electrode, a hole transporting layer proximate the first electrode, a second electrode, an electron transporting layer proximate the second electrode, and an emissive layer between the hole transporting layer and the electron transporting layer. The emissive layer includes a square planar tetradentate platinum or palladium complex, and excimers formed by two or more of the complexes are aligned such that emitting dipoles of the excimers are substantially parallel to a surface of the substrate.

Li, Jian↗
DOE OSTI2023

Low-loss interconnects for modular superconducting quantum processors

Low-loss superconducting aluminium cables and on-chip impedance transformers can be used to link qubit modules and create superconducting quantum computing networks with high-fidelity intermodule state transfer. Scaling is now a key challenge in superconducting quantum computing. One solution is to build modular systems in which smaller-scale quantum modules are individually constructed and calibrated and then assembled into a larger architecture. This, however, requires the development of suitable interconnects. Here we report low-loss interconnects based on pure aluminium coaxial cables and on-chip impedance transformers featuring quality factors of up to 8.1 x 10 5 , which is comparable with the performance of our transmon qubits fabricated on a single-crystal sapphire substrate. We use these interconnects to link five quantum modules with intermodule quantum state transfer and Bell state fidelities of up to 99%. To benchmark the overall performance of the processor, we create maximally entangled, multiqubit Greenberger-Horne-Zeilinger states. The generated intermodule four-qubit Greenberger-Horne-Zeilinger state exhibits 92.0% fidelity. We also entangle up to 12 qubits in a Greenberger-Horne-Zeilinger state with 55.8 ± 1.8% fidelity, which is above the genuine multipartite entanglement threshold of 1/2.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

A 3D extra-large-pore zeolite enabled by 1D-to-3D topotactic condensation of a chain silicate

Zeolites are microporous silicates with a large variety of applications as catalysts, adsorbents, and cation exchangers. Stable silica-based zeolites with increased porosity are in demand to allow adsorption and processing of large molecules but challenge our synthetic ability. We report a new, highly stable pure silica zeolite called ZEO-3, which has a multidimensional, interconnected system of extra-large pores open through windows made by 16 and 14 silicate tetrahedra, the least dense polymorph of silica known so far. This zeolite was formed by an unprecedented one-dimensional to three-dimensional (1D-to-3D) topotactic condensation of a chain silicate. With a specific surface area of more than 1000 square meters per gram, ZEO-3 showed a high performance for volatile organic compound abatement and recovery compared with other zeolites and metal-organic frameworks.

Science & Technology - Other Topics↗
DOE OSTI2023

Efficient, Color-Stable, and Long-Lived White Organic Light-Emitting Diodes Utilizing Phosphorescent Molecular Aggregates

Highly efficient and stable single-stack hybrid white organic light-emitting diode (WOLED) devices are developed using two emissive layers: one with amber-colored phosphorescent molecular-aggregate emission from the Pd(II) complex, Pd(II) 7-(3-(pyridine-2-yl-κN)phenoxy-κC)(benzo-κC)([c]benzo[4,5]imidazo-κN)[1,2-a][1,5]naphthyridine, Pd3O8-Py5, and the other with blue fluorescence emission. Here, an optimized device structure achieves high color stability under various current densities, an external quantum efficiency (EQE) of 45.5%, a power efficiency of 97.4 Lm W -1 , and an estimated LT 95 (operational time to 95% of the initial luminance) of 50 744 h at an initial luminance of 1000 cd m -2 .

36 MATERIALS SCIENCE↗
DOE OSTI2022

Learning infinite-horizon average-reward restless multi-action bandits via index awareness

We consider the online restless bandits with average-reward and multiple actions, where the state of each arm evolves according to a Markov decision process (MDP), and the reward of pulling an arm depends on both the current state of the corresponding MDP and the action taken. Since finding the optimal control is typically intractable for restless bandits, existing learning algorithms are often computationally expensive or with a regret bound that is exponential in the number of arms and states. In this paper, we advocate \textit{index-aware reinforcement learning} (RL) solutions to design RL algorithms operating on a much smaller dimensional subspace by exploiting the inherent structure in restless bandits. Specifically, we first propose novel index policies to address dimensionality concerns, which are provably optimal. We then leverage the indices to develop two low-complexity index-aware RL algorithms, namely, (i) GM-R2MAB, which has access to a generative model; and (ii) UC-R2MAB, which learns the model using an upper confidence style online exploitation method. We prove that both algorithms achieve a sub-linear regret that is only polynomial in the number of arms and states. A key differentiator between our algorithms and existing ones stems from the fact that our RL algorithms contain a novel exploitation that leverages our proposed provably optimal index policies for decision-makings.

Xiong, Guojun↗
DOE OSTI2022

Room-Temperature Quantitative Quantum Sensing of Lithium Ions with a Radical-Embedded Metal–Organic Framework

Recent advancements in quantum sensing have sparked transformative detection technologies with high sensitivity, precision, and spatial resolution. Owing to their atomic-level tunability, molecular qubits and ensembles thereof are promising candidates for sensing chemical analytes. Here, in this study, we show quantum sensing of lithium ions in solution at room temperature with an ensemble of organic radicals integrated in a microporous metal–organic framework (MOF). The organic radicals exhibit electron spin coherence and microwave addressability at room temperature, thus behaving as qubits. The high surface area of the MOF promotes accessibility of the guest analytes to the organic qubits, enabling unambiguous identification of lithium ions and quantitative measurement of their concentration through relaxometric and hyperfine spectroscopic methods based on electron paramagnetic resonance (EPR) spectroscopy. The sensing principle presented in this work is applicable to other metal ions with nonzero nuclear spin.

36 MATERIALS SCIENCE↗