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Quantum harmonic oscillator (QHO) battery models have been studied with significant importance in the recent past because these batteries are experimentally realizable and have high ergotropy and capacity to store more than one quanta of energy. QHO battery models are reinvestigated here to answer a set of fundamental questions: Do such models have any benefit? Is unbounded charging possible? Does the use of a catalyst system enhance the energy transfer to quantum batteries? These questions are answered both numerically and analytically by considering a model that allows a laser to shine on a QHO charger that interacts with a QHO battery. In contrast to some of the existing works, the obtained answers are mostly negative. Specifically, in the present work, the laser frequency is tuned with the frequency of the global charger-battery system, which is affected by the interaction between QHOs. It is reported that for a fixed laser field amplitude $\textit{F}$, the battery can store more energy when tuned with the frequency of the global charger-battery system compared to energy stored by tuning the laser frequency with local frequencies of the charger and battery. The charging process

Practical energy harvesting (EH) based communication systems typically use a battery to temporarily store the harvested energy prior to its use for communication. The batteries can be damaged when they are repeatedly charged (discharged) after being partially discharged (charged), overcharged or deeply discharged. This motivates the cycle constraint which says that a battery must be charged (discharged) only after it is sufficiently discharged (charged). We also assume Bernoulli energy arrivals, and a half-duplex constraint due to which the batteries are not charged and discharged simultaneously. In this context, we study EH communication systems with: (a) a single-battery with capacity 2B units and (b) dual-batteries, each having capacity of B units. The aim is to obtain the best possible long-term average throughputs and throughput regions in point-to-point (P2P) channels and multiple access channels (MAC), respectively. For the P2P channel, we obtain an analytical optimal solution in the single-battery case, and propose optimal and sub-optimal power allocation policies for the dual-battery case. We extend these policies to obtain achievable throughput regions in MACs by jointly

We establish a general implementation-independent approach to assess the potential advantage of using highly entangled quantum states between the initial and final states of the charging protocol to enhance the maximum charging power of quantum batteries. It is shown that the impact of entanglement on power can be separated from both the global quantum speed limit associated to an optimal choice of driving Hamiltonian and the energy gap of the batteries. We then demonstrate that the quantum state advantage of battery charging, defined as the power obtainable for given quantum speed limit and battery energy gap, is not an entanglement monotone. A striking example we provide is that, counterintuitively, independent thermalization of the local batteries, completely destroying any entanglement, can lead to larger charging power than that of the initial maximally entangled state. Highly entangled states can thus also be potentially disadvantageous when compared to product states. We also demonstrate that taking the considerable effort of producing highly entangled states, such as W or $k$-locally entangled states, is not sufficient to obtain quantum-enhanced scaling behavior with the nu

We propose a design of a quantum battery exploiting the non-Hermitian Hamiltonian as a charger. In particular, starting with the ground or the thermal state of the interacting (non-interacting) Hamiltonian as the battery, the charging of the battery is performed via parity-time (PT)- and rotational-time (RT)-symmetric Hamiltonian to store energy. We report that such a quenching with a non-Hermitian Hamiltonian leads to an enhanced power output compared to a battery with a Hermitian charger. We identify the region in the parameter space which provides the gain in performance. We also demonstrate that the improvements persist with the increase of system size for batteries with both PT- and RT-symmetric chargers. In the PT-symmetric case, although the anisotropy of the XY model does not help in the performance, we show that the XXZ model as a battery with a non-Hermitian charger performs better than that of the XX model having certain interaction strengths. We also exhibit that the advantage of non-Hermiticity remains valid even at finite temperatures in the initial states.

The participation of oxygen or other anionic species in redox activities in cathode materials for lithium and sodium-ion battery systems is known to play a role in governing the useful capacity of these batteries. Directly probing anionic redox mechanisms is not possible, rather the computational analysis by density functional theory poses the main approach towards gleaning insights into anionic redox activity and harnessing these effects to maximize capacity in future electrode materials. Here we showcase material systems exhibiting this mechanism of ion insertion and removal, and present the key computational considerations in studying anionic redox activities in battery materials. Aided by new computationally derived understandings of the role of anionic redox in emerging battery materials, increasingly greater levels of useable capacities can be extracted through informed materials design.

Effective thermal management is critical for lithium-ion battery packs' safe and efficient operations, particularly in applications such as drones, where compact designs and varying airflow conditions present unique challenges. This study investigates the thermal performance of a 16-cell lithium-ion battery pack by optimizing cooling airflow configurations and integrating phase change materials (PCMs) for enhanced heat dissipation. Seven geometric configurations were evaluated under airflow speeds ranging from 0 to 15 m/s, reflecting the operational conditions of civilian drones. A comprehensive 3D simulation approach was used to analyze the effects of inlet and outlet configurations, airflow dynamics, and PCM phase transition behavior. Results indicate that the trapezoidal (wide-base) configuration, paired with a 5-inlet and 1-outlet setup, achieves the most balanced performance, effectively maintaining optimal operating temperatures across low and high-speed airflow conditions. PCM integration further stabilized thermal behavior, with phase change durations extending to 12.5 min under tested conditions. These findings highlight the importance of geometric optimization and materia

Inverter-based resources (IBR) have been widely studied for their advantages on the current power systems. This increase in the penetration of renewable energy has raised some concerns about the stability of the existing grid. Historically, power systems are dominated by synchronous generators that can easily react to system instability due to high inertia and damping characteristics. However, with IBR, the control of the inverter plays a crucial role in contributing to the system stability and enhancing the functionality of the inverters. One of these novel control methods is droop control. Droop characteristics are used to control voltage, frequency, and active and reactive power. This paper presents the impact of frequency droop damping on system frequency, real power, and the rate of change of frequency with distributed energy resources. Also, battery sizing is suggested based on the results. The results also show the need for optimal selection for the frequency droop damping to fulfill the appropriate battery size in terms of cost and performance. The simulations are carried out in an electromagnetic transient program (EMTP)

This chapter illustrates the use of defect physics as a conceptual and theoretical framework for understanding and designing battery materials. It starts with a methodology for first-principles studies of defects in complex transition-metal oxides. The chapter then considers defects that are activated in a cathode material during synthesis, during measurements, and during battery use. Through these cases, it discusses possible defect landscapes in the material and their implications, guidelines for materials design via defect-controlled synthesis, mechanisms for electronic and ionic conduction and for electrochemical extraction and (re-)insertion, and effects of doping. Although specific examples are taken from studies of battery cathode materials, the computational approach and discussions are general and applicable to any ionic, electronic, or mixed ionic-electronic conducting materials.

Quantum control allows us to address the problem of engineering quantum dynamics for special purposes. While recently the field of quantum batteries has attracted much attention, optimization of their charging has not benefited from the quantum control methods. Here we fill this gap by using an optimization method. We apply for the first time this convergent iterative method for the control of the population of a bipartite quantum system in two cases, starting with a qubit-qubit case. The quantum charger-battery system is considered here, where the energy is pumped into the charger by an external classical electromagnetic field. Secondly, we systematically develop the original formulation of the method for two harmonic oscillators in the Gaussian regime. In both cases, the charger is considered to be an open dissipative system. Our optimization takes into account experimentally viable problem of turning-on and off of the charging external field. Optimising the shape of the pulse significantly boosts both the power and efficiency of the charging process in comparison to the sinusoidal drive. The harmonic oscillator setting of quantum batteries is of a particular interest, as the opt

Electrochemical and equivalent-circuit modelling are the two most popular approaches to battery simulation, but the former is computationally expensive and the latter provides limited physical insight. A theoretical middle ground would be useful to support battery management, on-line diagnostics, and cell design. We analyse a thermodynamically consistent, isothermal porous-electrode model of a discharging lead-acid battery. Asymptotic analysis of this full model produces three reduced-order models, which relate the electrical behaviour to microscopic material properties, but simulate discharge at speeds approaching an equivalent circuit. A lumped-parameter model, which neglects spatial property variations, proves accurate for C-rates below 0.1C, while a spatially resolved higher-order solution retains accuracy up to 5C. The problem of parameter estimation is addressed by fitting experimental data with the reduced-order models.

Cosmological and astrophysical observations indicate the presence of magnetic field over all scales. In order to explain these magnetic fields, it is assumed that there exists a seed magnetic field that gets amplified by dynamos. These seed fields may have been produced during inflation, at phase transitions, or some turbulent phase of the early universe. One well-known mechanism to get the seed field is the Biermann battery, which was originally discussed in the context of generation in an astrophysical object. Requirements for this mechanism to work are (i) non-zero gradient of the electron number density and pressure, (ii) they are non-parallel to each other. In the present article, we propose a similar mechanism to generate the seed field but in inhomogeneous chiral plasma. Our mechanism works, in presence of chiral anomaly, by the virtue of inhomogeneity in the chiral chemical potential and temperature. We will discuss various scenarios where inhomogeneities in the chemical potential and temperature can arise. We found that, depending on the epoch of generation, the strength of the seed magnetic fields varies from a few nano-Gauss (nG) to a few hundred nG.

An isothermal porous-electrode model of a discharging lead-acid battery is presented, which includes an extension of concentrated-solution theory that accounts for excluded-volume effects, local pressure variation, and a detailed microscopic water balance. The approach accounts for three typically neglected physical phenomena: convection, pressure diffusion, and variation of liquid volume with state of charge. Rescaling of the governing equations uncovers a set of fundamental dimensionless parameters that control the battery's response. Total volume change during discharge and nonuniform pressure prove to be higher-order effects in cells where variations occur in just one spatial dimension. A numerical solution is developed and exploited to predict transient cell voltages and internal concentration profiles in response to a range of C-rates. The dependence of discharge capacity on C-rate deviates substantially from Peukert's simple power law: charge capacity is concentration-limited at low C-rates, and voltage-limited at high C-rates. The model is fit to experimental data, showing good agreement.

We introduce a cross-domain behavioural assay of monitoring-control coupling in LLMs, grounded in the Nelson and Narens (1990) metacognitive framework and applying human psychometric methodology to LLM evaluation. The battery comprises 524 items across six cognitive domains (learning, metacognitive calibration, social cognition, attention, executive function, prospective regulation), each grounded in an established experimental paradigm. Tasks T1-T5 were pre-registered on OSF prior to data collection; T6 was added as an exploratory extension. After every forced-choice response, dual probes adapted from Koriat and Goldsmith (1996) ask the model to KEEP or WITHDRAW its answer and to BET or decline. The critical metric is the withdraw delta: the difference in withdrawal rate between incorrect and correct items. Applied to 20 frontier LLMs (10,480 evaluations), the battery discriminates three profiles consistent with the Nelson-Narens architecture: blanket confidence, blanket withdrawal, and selective sensitivity. Accuracy rank and metacognitive sensitivity rank are largely inverted. Retrospective monitoring and prospective regulation appear dissociable (r = .17, 95% CI wide given n=20

Monitoring the health of lithium-ion batteries' internal components as they age is crucial for optimizing cell design and usage control strategies. However, quantifying component-level degradation typically involves aging many cells and destructively analyzing them throughout the aging test, limiting the scope of quantifiable degradation to the test conditions and duration. Fortunately, recent advances in physics-informed machine learning (PIML) for modeling and predicting the battery state of health demonstrate the feasibility of building models to predict the long-term degradation of a lithium-ion battery cell's major components using only short-term aging test data by leveraging physics. In this paper, we present four approaches for building physics-informed machine learning models and comprehensively compare them, considering accuracy, complexity, ease-of-implementation, and their ability to extrapolate to untested conditions. We delve into the details of each physics-informed machine learning method, providing insights specific to implementing them on small battery aging datasets. Our study utilizes long-term cycle aging data from 24 implantable-grade lithium-ion cells subject

Metal components are extensively used as current collectors, anodes, and interlayers in lithium-ion batteries. Integrating these functions into one component enhances the cell energy density and simplifies its design. However, this multifunctional component must meet stringent requirements, including high and reversible Li storage capacity, rapid lithiation/delithiation kinetics, mechanical stability, and safety. Six single-atom metals (Mg, Zn, Al, Ag, Sn and Cu) are screened for lithiation behavior through their interaction with ion beams in electrochemically tested samples subjected to both weak and strong lithiation regimes. These different lithiation regimes allowed us to differentiate between the thermodynamics and kinetic aspects of the lithiation process. Three types of ions are used to determine Li depth profile: $H^+$ for nuclear reaction analysis (NRA), $He^+$ for Rutherford backscattering (RBS), and $Ga^+$ for focused ion beam (FIB) milling. The study reveals three lithiation behaviors: (i) Zn, Al, Sn form pure alloys with Li; (ii) Mg, Ag create intercalation solid solutions; (iii) Cu acts as a lithiation barrier. NRA and RBS offer direct and quantitative data, providing

The development of material acceleration platforms in battery research requires integrating complementary techniques and correlating heterogeneous experimental datasets. Here, this challenge is tackled in a large-scale multimodal program involving fifteen laboratories and facilities across Europe. Coordinated multi-site experiments are performed on state-of-the-art graphite / LiNiO2 Li-ion full cells to address two archetypal scientific questions: is the electrolyte composition impacting electrode properties, and how do electrode materials evolve when cells are cycled to their end-of-life? A fully standardized and centralized workflow is demonstrated, from sample production and delivery, to metadata and data handling, generating seventy-five concatenated datasets shared among all partners. Their integrated analysis shows that scientific conclusions depend critically on both the observable chosen to describe electrode properties, and the measurement technique employed. Individual experiments provide detailed information into specific aspects, such as crystal structures, redox activity, surface processes, morphology, etc., but can also function as binary diagnostic tool. Two-dimensio

This paper proposes a novel computationally efficient algorithm for optimal sizing of Battery Energy Storage Systems (BESS) considering renewable energy bidding strategies. Unlike existing two-stage methods, our algorithm enables the cooptimization of both by updating the BESS size during the training of the bidding policy, leveraging an extended reinforcement learning (RL) framework inspired by advancements in embodied cognition. By integrating the Deep Recurrent Q-Network (DRQN) with a distributed RL framework, the proposed algorithm effectively manages uncertainties in renewable generation and market prices while enabling parallel computation for efficiently handling long-term data.

Battery swapping as a business model for battery energy storage (BES) has great potential in future integrated low-carbon energy and transportation systems. However, frequent battery swapping will inevitably accelerate battery degradation and shorten the battery life accordingly. To model the tradeoff of BES use between energy and transportation applications coupled by battery swapping, we develop a life-cycle decision model that coordinates battery charging and swapping. This model is derived based on an improved intertemporal decision framework, in which the optimal marginal degradation cost (MDC) of BES is determined to maximize the BES benefit across time and application. The proposed framework and model are applied to manage a battery swapping station that simultaneously provides battery swapping services to electric vehicle customers and provides flexibility service to the power grid, including energy arbitrage and reserve. The case study shows that while the end of the physical life of BES occurs faster with battery swapping, the economic life becomes considerably longer. The results also reveal that the optimal MDC depends on the battery values in each application, and we a

Accurately predicting aging of lithium-ion batteries would help to prolong their lifespan, but remains a challenge owing to the complexity and interrelation of different aging mechanisms. As a result, aging prediction often relies on empirical or data-driven approaches, which obtain their performance from analyzing large datasets. However, these datasets are expensive to generate and the models are agnostic of the underlying physics and thus difficult to extrapolate to new conditions. In this article, a physical model is used to predict capacity fade caused by solid-electrolyte interphase (SEI) growth in 62 automotive cells, aged with 28 different protocols. Three protocols parametrize the time, current and temperature dependence of the model, the state of charge dependence results from the anode's open circuit voltage curve. The model validation with the remaining 25 protocols shows a high predictivity with a root-mean squared error of $1.28\%$. A case study with the so-validated model shows that the operating window, i.e. maximum and minimum state of charge, has the largest impact on SEI growth, while the influence of the applied current is almost negligible. Thereby the presente

This paper addresses the problem of planning complex manipulation tasks, in which multiple robots with different end-effectors and capabilities, informed by computer vision, must plan and execute concatenated sequences of actions on a variety of objects that can appear in arbitrary positions and configurations in unstructured scenes. We propose an intent-driven planning pipeline which can robustly construct such action sequences with varying degrees of supervisory input from a human using simple language instructions. The pipeline integrates: (i) perception-to-text scene encoding, (ii) an ensemble of large language models (LLMs) that generate candidate removal sequences based on the operator's intent, (iii) an LLM-based verifier that enforces formatting and precedence constraints, and (iv) a deterministic consistency filter that rejects hallucinated objects. The pipeline is evaluated on an example task in which two robot arms work collaboratively to dismantle an Electric Vehicle battery for recycling applications. A variety of components must be grasped and removed in specific sequences, determined by human instructions and/or by task-order feasibility decisions made by the autonom