Development of lithium-ion battery state estimation techniques for battery management systems

University of Technology Sydney. Faculty of Engineering and Information Technology.Lithium-ion batteries are being widely used as an enabling energy storage for electric vehicles, renewable energy storage systems, and power grids, etc., as they always exhibit high energy density and long life cycle along with environmental friendliness. However, overly pessimistic or optimistic estimates of lithium-ion battery states would result in waste or abuse of battery available capabilities and may even lead to fire and explosion risks. The safety and reliability of battery utilization necessitate the accurate and reliable state estimation techniques in battery management systems (BMSs). This thesis focuses on the development of the estimation methods of lithium-ion battery states of interest, which are capable of determining internal battery status accurately. The first phase of this thesis centers on battery electrochemical model simplification and discretization for incorporating the co-estimation algorithm of battery state of charge (SOC), capacity, and resistance based on the proportional-integral (PI) observers. A physics-based battery model that has the capability to describe the electrochemical reaction process inside the battery is first developed. Trinal PI observers are then employed to implement the co-estimation task. It takes the influence of battery aging on SOC estimation by furnishing the state equations with up-to-date capacity and resistance estimates into account, thereby improving the SOC estimation accuracy. To achieve high estimation accuracy with low computation costs, SOC and capacity estimation approaches based on the incremental capacity analysis and differential voltage analysis are subsequently investigated. Feature points extracted from the SOC based incremental capacity/differential voltage (DV) curves are applied for developing the estimation algorithm of battery SOC and capacity. Besides, an extended Kalman filter and a particle filter are served as the state observers in an SOC estimator based on the DV model for further improving the performance of estimation. With the credible SOC estimates, a state of energy (SOE) estimator based on a quantitative relationship between SOC and SOE is proposed in the next step, and a moving-window energy-integral technique is then incorporated to estimate the battery maximum available energy. Through the analysis of ambient temperature, battery discharge/charge current rate, and cell aging level dependencies of SOE, the relationship between SOC and SOE can be quantified as a quadratic function for SOE estimation. The simplicity of the proposed SOE estimation method can avoid the heavy computation cost required by the conventional model-based SOE estimation methods. Finally, two state of power capability predictors are designed for a battery to sufficiently absorb or deliver a certain amount of power within its safe operating region. A battery direct current resistance model for quantitatively describing its temperature dependence is proposed and implemented on the battery capability prediction in the first method, which is beneficial to reduce the memory-consumption and dimension of the power characteristic map embedded in BMSs for applications. Different from the conventional methods using the limits of macroscopically observed variables for power prediction, the second method investigates a physical mechanism-based power prediction method and quantifies the relationship between battery power capability and surface lithium concentration for instantaneous peak power prediction. The proposed methods are experimentally verified with various cell aging levels and ambient temperatures. The proposed approaches for accurately modelling and estimating lithium-ion battery states in this thesis can contribute to safe, reliable and sufficient utilization of the battery. The developed methods are pretty general, and therefore are promising to provide valuable insight to the investigations of other types of batteries with various chemistries

Tracking Berry curvature effect in molecular dynamics by ultrafast magnetic x-ray scattering

The spin-dependent Berry force is a genuine effect of Berry curvature in molecular dynamics, which can dramatically result in spatial spin separation and change of reaction pathways. However, the way to probe the effect of Berry force remains challenging, because the time-reversal (TR) symmetry required for opposite Berry forces conflicts with TR symmetry breaking spin alignment needed to observe the effect, and the net effect could be transient for a molecular wave packet. We demonstrate that in molecular photodissociation, the dissociation rates can be different for molecules with opposite initial spin directions due to Berry force. We showcase that the spatially separated spin density, which is transiently induced by Berry force as the molecular wave packet passes through conical intersection, can be reconstructed from the circular dichroism (CD) of ultrafast non-resonant magnetic x-ray scattering using free electron lasers

Robustness of SOC Estimation Algorithms for EV Lithium-Ion Batteries against Modeling Errors and Measurement Noise

State of charge (SOC) is one of the most important parameters in battery management system (BMS). There are numerous algorithms for SOC estimation, mostly of model-based observer/filter types such as Kalman filters, closed-loop observers, and robust observers. Modeling errors and measurement noises have critical impact on accuracy of SOC estimation in these algorithms. This paper is a comparative study of robustness of SOC estimation algorithms against modeling errors and measurement noises. By using a typical battery platform for vehicle applications with sensor noise and battery aging characterization, three popular and representative SOC estimation methods (extended Kalman filter, PI-controlled observer, and H∞ observer) are compared on such robustness. The simulation and experimental results demonstrate that deterioration of SOC estimation accuracy under modeling errors resulted from aging and larger measurement noise, which is quantitatively characterized. The findings of this paper provide useful information on the following aspects: (1) how SOC estimation accuracy depends on modeling reliability and voltage measurement accuracy; (2) pros and cons of typical SOC estimators in their robustness and reliability; (3) guidelines for requirements on battery system identification and sensor selections

CUEING: a lightweight model to Capture hUman attEntion In driviNG

Discrepancies in decision-making between Autonomous Driving Systems (ADS) and human drivers underscore the need for intuitive human gaze predictors to bridge this gap, thereby improving user trust and experience. Existing gaze datasets, despite their value, suffer from noise that hampers effective training. Furthermore, current gaze prediction models exhibit inconsistency across diverse scenarios and demand substantial computational resources, restricting their on-board deployment in autonomous vehicles. We propose a novel adaptive cleansing technique for purging noise from existing gaze datasets, coupled with a robust, lightweight convolutional self-attention gaze prediction model. Our approach not only significantly enhances model generalizability and performance by up to 12.13% but also ensures a remarkable reduction in model complexity by up to 98.2% compared to the state-of-the art, making in-vehicle deployment feasible to augment ADS decision visualization and performance

Uni-QSAR: an Auto-ML Tool for Molecular Property Prediction

Recently deep learning based quantitative structure-activity relationship (QSAR) models has shown surpassing performance than traditional methods for property prediction tasks in drug discovery. However, most DL based QSAR models are restricted to limited labeled data to achieve better performance, and also are sensitive to model scale and hyper-parameters. In this paper, we propose Uni-QSAR, a powerful Auto-ML tool for molecule property prediction tasks. Uni-QSAR combines molecular representation learning (MRL) of 1D sequential tokens, 2D topology graphs, and 3D conformers with pretraining models to leverage rich representation from large-scale unlabeled data. Without any manual fine-tuning or model selection, Uni-QSAR outperforms SOTA in 21/22 tasks of the Therapeutic Data Commons (TDC) benchmark under designed parallel workflow, with an average performance improvement of 6.09\%. Furthermore, we demonstrate the practical usefulness of Uni-QSAR in drug discovery domains

Dendrimer-entrapped gold nanoparticles as potential CT contrast agents for blood pool imaging

The purpose of this study was to evaluate dendrimer-entrapped gold nanoparticles [Au DENPs] as a molecular imaging [MI] probe for computed tomography [CT]. Au DENPs were prepared by complexing AuCl4- ions with amine-terminated generation 5 poly(amidoamine) [G5.NH2] dendrimers. Resulting particles were sized using transmission electron microscopy. Serial dilutions (0.001 to 0.1 M) of either Au DENPs or iohexol were scanned by CT in vitro. Based on these results, Au DENPs were injected into mice, either subcutaneously (10 μL, 0.007 to 0.02 M) or intravenously (300 μL, 0.2 M), after which the mice were imaged by micro-CT or a standard mammography unit. Au DENPs prepared using G5.NH2 dendrimers as templates are quite uniform and have a size range of 2 to 4 nm. At Au concentrations above 0.01 M, the CT value of Au DENPs was higher than that of iohexol. A 10-μL subcutaneous dose of Au DENPs with [Au] ≥ 0.009 M could be detected by micro-CT. The vascular system could be imaged 5 and 20 min after injection of Au DENPs into the tail vein, and the urinary system could be imaged after 60 min. At comparable time points, the vascular system could not be imaged using iohexol, and the urinary system was imaged only indistinctly. Findings from this study suggested that Au DENPs prepared using G5.NH2 dendrimers as templates have good X-ray attenuation and a substantial circulation time. As their abundant surface amine groups have the ability to bind to a range of biological molecules, Au DENPs have the potential to be a useful MI probe for CT

Entangled X-ray Photon Pair Generation by Free Electron Lasers

Einstein, Podolsky and Rosen's prediction on incompleteness of quantum mechanics was overturned by experimental tests on Bell's inequality that confirmed the existence of quantum entanglement. In X-ray optics, entangled photon pairs can be generated by X-ray parametric down conversion (XPDC), which is limited by relatively low efficiency. Meanwhile, free electron laser (FEL) has successfully lased at X-ray frequencies recently. However, FEL is usually seen as a classical light source, and its quantum effects are considered minor corrections to the classical theory. Here we investigate entangled X-ray photon pair emissions in FEL. We establish a theory for coherently amplified entangled photon pair emission from microbunched electron pulses in the undulator. We also propose an experimental scheme for the observation of the entangled photon pairs via energy and spatial correlation measurements. Such an entangled X-ray photon pair source is of great importance in quantum optics and other X-ray applications.Comment: 13 pages, 3 figure

Janus-graphene: a two-dimensional half-auxetic carbon allotropes with non-chemical Janus configuration

The asymmetric properties of Janus two-dimensional materials commonly depend on chemical effects, such as different atoms, elements, material types, etc. Herein, based on carbon gene recombination strategy, we identify an intrinsic non-chemical Janus configuration in a novel purely sp$^2$ hybridized carbon monolayer, named as Janus-graphene. With the carbon gene of tetragonal, hexagonal, and octagonal rings, the spontaneous unilateral growth of carbon atoms drives the non-chemical Janus configuration in Janus-graphene, which is totally different from the chemical effect in common Janus materials such as MoSSe. A structure-independent half-auxetic behavior is mapped in Janus-graphene that the structure maintains expansion whether stretched or compressed, which lies in the key role of $p_z$ orbital. The unprecedented half-auxeticity in Janus-graphene extends intrinsic auxeticity into pure sp$^2$ hybrid carbon configurations. With the unique half-auxeticity emerged in the non-chemical Janus configuration, Janus-graphene enriches the functional carbon family as a promising candidate for micro/nanoelectronic device applications