Updated Parameters Research Articles

Improved thermonuclear rate of42Ti(p,γ)43V and its astrophysical implication in therpprocess

Context.Accurate42Ti(p,γ)43V reaction rates are crucial for understanding the nucleosynthesis path of the rapid capture process (rpprocess) that occurs in X-ray bursts.Aims.We aim to improve the thermonuclear rates of42Ti(p,γ)43V based on more complete resonance information and a more accurate direct component, together with the recently released nuclear masses data. We also explore the impact of the newly obtained rates on therpprocess.Methods.We reevaluated the reaction rate of42Ti(p,γ)43V by the sum of the isolated resonance contribution instead of the Hauser-Feshbach statistical model. We used a Monte Carlo method to derive the associated uncertainties of new rates. The nucleosynthesis simulations were performed via the NuGrid post-processing code ppn.Results.The new rates differ from previous estimations due to the use of a series of updated resonance parameters and a direct S factor. Compared with the previous results from the Hauser-Feshbach statistical model, which assumes compound nucleus43V with a sufficiently high-level density in the energy region of astrophysical interest, large differences exist over the entire temperature region ofrp-process interest, up to two orders of magnitude. We consistently calculated the photodisintegration rate using our new nuclear masses via the detailed balance principle, and found the discrepancies among the different reverse rates are much larger than those for the forward rate, up to ten orders of magnitude at the temperature of 108K. Using a trajectory with a peak temperature of 1.95×109K, we performed therp-process nucleosynthesis simulations to investigate the impact of the new rates. Our calculations show that the adoption of the new forward and reverse rates result in abundance variations for Sc and Ca of 128% and 49%, respectively, compared to the variations for the statistical model rates. On the other hand, the overall abundance pattern is not significantly affected. The results of using new rates also confirm that therp-process path does not bypass the isotope43V.Conclusions.Our study found that the Hauser-Feshbach statistical model is inappropriate to the reaction rate evaluation for42Ti(p,γ)43V. The adoption of the new rates confirms that the reaction path of42Ti(p,γ)43V(p,γ)44Cr(β+)44V is a key branch of therpprocess in X-ray bursts.

Multi-H∞ Controls for Unknown Input-Interference Nonlinear System With Reinforcement Learning.

This article studies the multi-H∞ controls for the input-interference nonlinear systems via adaptive dynamic programming (ADP) method, which allows for multiple inputs to have the individual selfish component of the strategy to resist weighted interference. In this line, the ADP scheme is used to learn the Nash-optimization solutions of the input-interference nonlinear system such that multiple H∞ performance indices can reach the defined Nash equilibrium. First, the input-interference nonlinear system is given and the Nash equilibrium is defined. An adaptive neural network (NN) observer is introduced to identify the input-interference nonlinear dynamics. Then, the critic NNs are used to learn the multiple H∞ performance indices. A novel adaptive law is designed to update the critic NN weights by minimizing the Hamiltonian-Jacobi-Isaacs (HJI) equation, which can be used to directly calculate the multi-H∞ controls effectively by using input-output data such that the actor structure is avoided. Moreover, the control system stability and updated parameter convergence are proved. Finally, two numerical examples are simulated to verify the proposed ADP scheme for the input-interference nonlinear system.

Stochastic Approximation for Multi-period Simulation Optimization with Streaming Input Data

We consider a continuous-valued simulation optimization (SO) problem, where a simulator is built to optimize an expected performance measure of a real-world system while parameters of the simulator are estimated from streaming data collected periodically from the system. At each period, a new batch of data is combined with the cumulative data and the parameters are re-estimated with higher precision. The system requires the decision variable to be selected in all periods. Therefore, it is sensible for the decision-maker to update the decision variable at each period by solving a more precise SO problem with the updated parameter estimate to reduce the performance loss with respect to the target system. We define this decision-making process as the multi-period SO problem and introduce a multi-period stochastic approximation (SA) framework that generates a sequence of solutions. Two algorithms are proposed: Re-start SA ( ReSA ) reinitializes the stepsize sequence in each period, whereas Warm-start SA ( WaSA ) carefully tunes the stepsizes, taking both fewer and shorter gradient-descent steps in later periods as parameter estimates become increasingly more precise. We show that under suitable strong convexity and regularity conditions, ReSA and WaSA achieve the best possible convergence rate in expected sub-optimality either when an unbiased or a simultaneous perturbation gradient estimator is employed, while WaSA accrues significantly lower computational cost as the number of periods increases. In addition, we present the regularized ReSA which obviates the need to know the strong convexity constant and achieves the same convergence rate at the expense of additional computation.

Revisiting experimental techniques and theoretical models for estimating the solubility parameter of rubbery and glassy polymer membranes

Estimation and correlation of the Hildebrand solubility parameter (δ) of polymers and small molecules is a common practice in membrane material science and is accomplished by experimental and numerical routes. In this paper, we revisit, update, and compare both routes to enhance the accuracy in the determination of δ. Best practices for the experimental determination of polymer solubility parameters are provided, and the viability of Dynamic Light Scattering (DLS) was demonstrated as an alternative to conventional time- and material-consuming techniques, such as Ubbelohde viscometry and swelling measurements. Glassy and rubbery polymers, including high fractional free volume (FFV) microporous polymers such as PIM-1 and poly(1-trimethylsilyl-1-propyne) (PTMSP), are among the samples included in this study with great relevance to membrane science. In an attempt to enhance the accuracy of numerical estimate of polymer solubility parameters via the group contribution method, we provide updated group contribution parameters, along with their uncertainty, according to the technique recently reported by Smith et al. These updated group contribution parameters result in a mean absolute relative error of 9.0% in predicting the solubility parameter on a test set of 40 polymers, which is on par with the average 10% error reported previously. We also show, using machine learning techniques, that augmenting the group contribution model with extra parameters or non-linear relationships does not improve its accuracy. Results of the updated group contribution technique and dynamic light scattering measurements were compared to experimental viscometry on four test polymers, and the difference between the three techniques is compared.

Chlorophyll-based model underpinned by measured inherent optical properties of Jerlov water types.

An existing chlorophyll-based model has been updated and re-calibrated using measured data describing Jerlov water types, harvested from the World-wide Ocean Optics Database. This study has provided new chlorophyll concentration data, and used them in conjunction with recently published spectra of absorption and scattering coefficients to create an updated parameter set that describes eight of the 10 Jerlov water types. The updated model is consistent with other data, and it interprets the measured characteristics in terms of underlying properties. Techniques for inter-conversion between inherent and apparent optical properties have been further investigated, and the improved precision has uncovered new challenges that have been addressed using empirical techniques.

Toward Adaptive Semantic Communications: Efficient Data Transmission via Online Learned Nonlinear Transform Source-Channel Coding

The emerging field semantic communication is driving the research of end-to-end data transmission. By utilizing the powerful representation ability of deep learning models, learned data transmission schemes have exhibited superior performance than the established source and channel coding methods. While, so far, research efforts mainly concentrated on architecture and model improvements toward a static target domain. Despite their successes, such learned models are still suboptimal due to the limitations in model capacity and imperfect optimization and generalization, particularly when the testing data distribution or channel response is different from that adopted for model training, as is likely to be the case in real-world. To tackle this, in this paper, we propose a novel online learned joint source and channel coding approach that leverages the deep learning model’s overfitting property. Specifically, we update the off-the-shelf pre-trained models after deployment in a lightweight online fashion to adapt to the distribution shifts in source data and environment domain. We take the overfitting concept to the extreme, proposing a series of implementation-friendly methods to adapt the codec model or representations to an individual data or channel state instance, which can further lead to substantial gains in terms of the end-to-end rate-distortion performance. Accordingly, the streaming ingredients include both the semantic representations of source data and the online updated decoder model parameters. The system design is formulated as a joint optimization problem whose goal is to minimize the loss function, a tripartite trade-off among the data stream bandwidth cost, model stream bandwidth cost, and end-to-end distortion. The proposed methods enable the communication-efficient adaptation for all parameters in the network without sacrificing decoding speed. Extensive experiments, including user study, on continually changing target source data and wireless channel environments, demonstrate the effectiveness and efficiency of our approach, on which we outperform existing state-of-the-art engineered transmission scheme (VVC combined with 5G LDPC coded transmission).

A Systematic Approach for Inertial Sensor Calibration of Gravity Recovery Satellites and Its Application to Taiji-1 Mission

High-precision inertial sensors or accelerometers can provide references for free-falling motion in gravitational fields in space. They serve as the key payloads for gravity recovery missions such as CHAMP, the GRACE-type missions, and the planned Next-Generation Gravity Missions. In this work, a systematic method for electrostatic inertial sensor calibration of gravity recovery satellites is suggested, which is applied to and verified with the Taiji-1 mission. With this method, the complete operating parameters including the scale factors, the center of mass offset vector, and the intrinsic biased acceleration can be precisely calibrated with only two sets of short-term in-orbit experiments. This could reduce the gaps in data that are caused by necessary in-orbit calibrations during the lifetime of related missions. Taiji-1 is the first technology-demonstration satellite of the “Taiji Program in Space”, which, in its final extended phase in 2022, could be viewed as operating in the mode of a high–low satellite-to-satellite tracking gravity mission. Based on the principles of calibration, swing maneuvers with time spans of approximately 200 s and rolling maneuvers for 19 days were conducted by Taiji-1 in 2022. Given the data of the actuation voltages of the inertial sensor, satellite attitude variations, precision orbit determinations, the inertial sensor’s operating parameters are precisely re-calibrated with Kalman filters and are relayed to the Taiji-1 science team. The relative errors of the calibrations are <1% for the linear scale factors, <3% for center of mass, and <0.1% for biased accelerations. Data from one of the sensitive axes are re-processed with the updated operating parameters, and the resulting performance is found to be slightly improved over the former results. This approach could be of high reference value for the accelerometer or inertial sensor calibrations of the GFO, the Chinese GRACE-type mission, and the Next-Generation Gravity Missions. This could also create some insight into the in-orbit calibrations of the ultra-precision inertial sensors for future GW space antennas because of the technological inheritance between these two generations of inertial sensors.

Estimating stratospheric carbon from fires during a regional nuclear exchange

Although fires are the primary source for black carbon (BC) from a regional nuclear exchange, climate simulations have traditionally approximated the source term for scenario estimates. While approaches and suggested parameters were outlined by the U.S. National Academies of Science study in 1985, there has not been an update to these recommendations. The emissions from fires induced by the detonation of a nuclear weapon in an urban environment involve significant complexities, and this manuscript provides a survey of updated parameters for climate estimates and a critical evaluation of their validity in the context of the large-scale fires anticipated from such an event. The purpose of this work is to help progress the methodologies for estimating BC sources and stratospheric injection and to guide the application of uncertainties and more advanced modeling towards improved fire consequence estimates in support of the hazard assessments.

Transmission probability filter optimization for Agility MLC in Monaco treatment planning system.

In the Monte Carlo-based treatment planning system (TPS) Monaco, transmission probability filters (TPF) are utilized to describe the transmission through the multi leaf collimator (MLC). By having knowledge of the TPF parameters for various photon beam energies, adjusting the MLC transmission parameters becomes easier, enhancing the accuracy of the Monte Carlo algorithm in achieving a dose distribution that closely aligns with the irradiated dose at the Versa HD linear accelerator (linac). The objective of this study was to determine the TPF parameters for 6MV, 10MV, 6MV flattening filter free (FFF) and 10MV FFF for a Versa HD linac equipped with Agility MLC. The TPF parameters were adjusted using point dose measurements and vendor-provided fields specifically designed to fine-tune the MLC. After adjusting the TPF parameters, a gamma passing rate (GPR) analysis was conducted on 25 treatment plans to ensure that the Monte Carlo model, with the updated TPF parameters, accurately matched the actual linac delivery. The TPF values ranged from 0.0018 to 0.0032 for leaf transmission and 1.15 to 1.25 for Leaf Tip leakage across the different energies. The average GPR ranged from 97.8% for 10MV FFF to 98.5% for 6MV photon energies. Additionally, the TPF parameters for 6MV obtained in this study were consistent with previously published TPF values for 6MV photon energy. Hence, it was concluded that optimizing the TPF does not need to be performed for every individual Versa HD linac with Agility MLC. Instead, the published parameters can be applied to other Versa HD linacs to enhance clinical accuracy. In conclusion, this study determined the TPF parameters for 6MV and previously unpublished photon energies 10MV, 6MV FFF and 10MV FFF. These parameters can be easily transferred to other facilities, resulting in improved agreement between the dose distribution from the TPS and the linac.

Dynamic Parameters Updating of Earth-rock Dams Based on Modal Parameter Identification

The reasonable selection of dynamic parameters of earth-rock dams is very important for the seismic safety assessment of dams. Because of the size effects, the parameters acquired from the indoor test may additionally no longer exactly reflect the real dynamic characteristics of the dam. Therefore, it is essential to correct and update the dynamic parameters in accordance to the actual seismic dynamic characteristics of the dam. In this paper, by combining the Covariance-driven stochastic subspace identification (SSI-COV) method and the multiple population genetic algorithm (MPGA), an optimization and updating method for the dynamic parameters of earth-rock dams based on modal parameter identification is presented. The collected weak earthquake records of the dam allowed the modal parameters (the natural frequencies and mode shapes) identification based on SSI-COV method. The optimization updated is performed using the MPGA, which can obtain the optimal values of dynamic parameters of the dam and has good robustness within the optimization range of the numerical model. A typical numerical example is given to exhibit the feasibility and effectiveness of the presented method. Then the dynamic parameters of Liyutan Dam are updated by the proposed method according to the weak earthquake records, and the accuracy and applicability of the updated parameters were further verified by the strong earthquake records. The results show that the method proposed in this paper can provide a good idea for the calibration of dynamic parameters of earth-rock dams and has well practical engineering value.

Intraoperative CBCT imaging in endovascular abdomen aneurysm repair – Optimization of exposure parameters using a stent phantom

Cone beam computed tomography (CBCT) may provide essential additional image guidance to endovascular abdominal aneurysm repair (EVAR) operations but also significant radiation exposure to patients if scans are not carefully optimized. The purpose of our study was to define the image quality requirements for intraoperative EVAR CBCT imaging and to optimize the CBCT exposure parameters accordingly. A Multi-Energy CT phantom simulating a large patient was used by replacing the central phantom cylinder with a custom water-filled insert including an EVAR stent. Different exposure parameters covering a range of radiation qualities and dose levels were used to define the optimal image quality level regarding stent graft evaluation (compressed, bent, or collapsed). The radiation dose was measured with a calibrated air kerma-area product (KAP) meter and organ doses were calculated based on Monte Carlo simulations and a mathematical patient model. Based on the results, updated exposure parameters with the highest mean energy and lowest dose level available were recommended. With the updated protocol, the radiation exposure could be significantly decreased. The KAP value decreased from 9720 μGy·m2 to 440 μGy·m2 and reference point air kerma from 351 mGy to 16 mGy (a reduction of 96%) and organ doses of the organs in the irradiated region decreased on an average 91%. The new protocol resulted in acceptable clinical image quality based on testing with clinical cases.

Vegetation Representation Influences Projected Streamflow Changes in the Colorado River Basin

Abstract Vegetation parameters for the Variable Infiltration Capacity (VIC) hydrologic model were recently updated using observations from the Moderate Resolution Imaging Spectroradiometer (MODIS). Previous work showed that these MODIS-based parameters improved VIC evapotranspiration simulations when compared to eddy covariance observations. Due to the importance of evapotranspiration within the Colorado River basin, this study provided a basin-by-basin calibration of VIC soil parameters with updated MODIS-based vegetation parameters to improve streamflow simulations. Interestingly, while both configurations had similar historic streamflow performance, end-of-century hydrologic projections, driven by 29 downscaled global climate models under the RCP8.5 emissions scenario, differed between the two configurations. The calibrated MODIS-based configuration had an ensemble mean that simulated little change in end-of-century annual streamflow volume (+0.4%) at Lees Ferry, Arizona, relative to the historical period (1960–2005). In contrast, the previous VIC configuration, which is used to inform decisions about future water resources in the Colorado River basin, projected an 11.7% decrease in annual streamflow. Both VIC configurations simulated similar amounts of evapotranspiration in the historical period. However, the MODIS-based VIC configuration did not show as much of an increase in evapotranspiration by the end of the century, primarily within the upper basin’s forested areas. Differences in evapotranspiration projections were the result of the MODIS-based vegetation parameters having lower leaf area index values and less forested area compared to previous vegetation estimates used in recent Colorado River basin hydrologic projections. These results highlight the need to accurately characterize vegetation and better constrain climate sensitivities in hydrologic models. Significance Statement Understanding systemic changes in annual Colorado River basin flows is critical for managing long-term reservoir levels. Single-digit percentage decreases have the potential to degrade the regions’ water supply, hydropower generation, and environmental concerns. Hydrology projections under climate change have largely been based on simulations from the Variable Infiltration Capacity model. Updating the model’s vegetation representation based on updated satellite information highlighted the sensitivity of the hydrologic projections to the models’ vegetation representation primarily within forested areas. This updated model did not increase in evapotranspiration by the end of the century as much as previous simulations. This increased the mean and ensemble spread of the projected streamflow changes, emphasizing the need to properly characterize the hydrologic model’s vegetation parameters and better constrain model climate sensitivity.

Bayesian Inference for State-Space Models With Student-t Mixture Distributions.

This article proposes a robust Bayesian inference approach for linear state-space models with nonstationary and heavy-tailed noise for robust state estimation. The predicted distribution is modeled as the hierarchical Student- t distribution, while the likelihood function is modified to the Student- t mixture distribution. By learning the corresponding parameters online, informative components of the Student- t mixture distribution are adapted to approximate the statistics of potential uncertainties. Then, the obstacle caused by the coupling of the updated parameters is eliminated by the variational Bayesian (VB) technique and fixed-point iterations. Discussions are provided to show the reasons for the achieved advantages analytically. Using the Newtonian tracking example and a three degree-of-freedom (DOF) hover system, we show that the proposed inference approach exhibits better performance compared with the existing method in the presence of modeling uncertainties and measurement outliers.

Physics-enhanced machine learning-based optimization of tuned mass damper parameters for seismically-excited buildings

Optimal design of tuned mass damper (TMD) parameters should be implemented before device installation to achieve the desired vibration reduction effects for tall and quite slender buildings. Although various optimization algorithms have been developed for searching TMD parameters, there has been very limited exploration of machine learning-based optimization approach in this domain. In this paper, two machine learning-based algorithms are proposed to optimize the natural frequency and damping ratio of a TMD device for seismically excited building. The first algorithm investigates the single-objective optimization of a TMD parameters on a linear building structure. It is based on physics-enhanced generative and adversarial network (GAN) architecture. A generative network randomly generates the primary TMD parameters within the specified parameter ranges, while an adversarial network guides the evolution of parameters to acquire updated parameters, a physical evaluation network assesses the performance of the updated and primary parameters, subsequently providing training data for both the generated network and adversarial network. Furthermore, a second physics-enhanced machine learning algorithm is proposed for the multi-objective optimization of a TMD parameters on a nonlinear building structure under various seismic excitations. The physical evaluation network is extended to consider multiple objective functions, in which the multiple objective functions are integrated through a kind of decomposition scheme, i.e., weighted sum approach. Then, a better set of parameters can be determined and a Pareto optimal solution is obtained. To verify the effectiveness of the two proposed algorithms, single-objective optimization of a TMD parameters for a linear shear-type structure under seismic excitation and a multiple-objectives optimization of a TMD parameters for a seismically excited nonlinear moment-resisting frame (MRF) are conducted and the optimizations are validated by comparing with those by the conventional particle swarm optimization (PSO) approach.

CULTURE PRODUCTIVITY OF Daphnia magna FED WITH QUAIL DROPPINGS (Coturnix coturnix)

This research aimed to know the effect of quail manure on an abundance of Daphnia magna. The research method for D. magna was a Completely Randomized Design (CRD) with four settings, each repeated four times, namely the use of P0 (Control without fertilizer), P1 (Quail manure fertilize 1 g/L), P2 (Quail manure fertilize 3 g/L), P3 (Quail manure fertilize 5 g/L). D. magna was cultured for 15 days in a container with a volume of 3 liters. The updated parameters were abundance of D. magna, mortality of D. magna, and water quality. The results were analyzed using ANOVA analysis. The results showed that the highest abundance was in P2 (3g / L), and the lowest was in P0 (control). Water quality during the research was temperature 21-24°C, pH 7,5-8,4, DO 6,2-7,8 ppm, and ammonia 0-0,25 mg/L.

Critical strain method incorporating calculation of equivalent H-B strength parameters for back analysis during tunnel excavation

A comprehensive method is proposed for the continuous update of the design rockmass parameters during tunnel excavation coupled to novel calculation of the Hoek-Brown strength parameters for given Mohr-Coulomb ones. The method starts with the strength parameters of Mohr-Coulomb or Hoek-Brown criterion used at the design stage of tunnels, and then updates them by using the displacements measured during the excavation of tunnels. The update is performed based on a well established back analysis procedure that determines both cohesion C and internal friction angle φ of the Mohr-Coulomb criterion. Then, these parameters either directly update or are converted to equivalent Hoek-Brown parameters that update the original design parameters of the tunnel. The need arising for conversion of parameters during the back analysis is thus addressed: A method is developed for the evaluation of the equivalent Hoek-Brown parameters for an axisymmetric structure subjected to major principal initial stress p0 and supported by internal pressure pi. Four characteristic cases are analysed theoretically and numerically with a finite differences code and found to be in excellent agreement. The equivalent parameters solution is developed in Matlab source code under a creative commons license and greatly enables the automation of the update procedure.Based on the updated parameters, it is possible to continuously modify the original design of underground support structures, according to the results of back analysis of measured displacements. The modified strength parameters of the H-B criterion can also be used for assessing the stability of the tunnels for the next excavation stage of the tunnels.

Finite Element Model Updating Using Resonance–Antiresonant Frequencies with Radial Basis Function Neural Network

The modal frequencies, model shapes or their derivatives are generally used as the characteristic quantities of the objective function for the finite element model (FEM) updating. However, the measurement accuracy of the model shapes is low due to the few numbers of measurement points for actual structures, which results in a large correction error. The antiresonant frequency reflects the local information of the structure more accurately than the mode shapes, which is a good complement to the resonance frequencies. In this paper, a FEM updating using resonance and antiresonant frequencies with radial basis function (RBF) neural network is proposed. The elastic modulus, added mass, tensile stiffness and torsional stiffness are selected as the updating parameters of FEM for a cantilever beam, which were grouped by the uniform design method. The resonance and antiresonant frequencies identified from the frequency response function (FRF) obtained from corresponding FEM at only one node are taken as the characteristic quantities. The RBF neural network is adopted to construct the mapping relationships between the characteristic quantities and the updating parameters. The updated parameters are substituted into the FEM, and the FRF is obtained to verify the validity of the method. The results show that the relative errors between all the updated parameters and the target values are less than 7%, and the relative errors of the characteristic quantities in the updating frequency band are less than 3%. The proposed method can accurately reproduce the dynamic characteristics of the cantilever beam. It can be applied to the damage detection and safety evaluation of large structures which are difficult to arrange more measuring points.

Clustered and Multi-Tasked Federated Distillation for Heterogeneous and Resource Constrained Industrial IoT Applications

Service heterogeneity, the diversity of services provided by different devices and systems in the Industrial Internet of Things (IIoT), makes communication and data exchange difficult and affects the IIoT performance. Artificial intelligence, particularly machine learning (ML), can address this challenge by analyzing data, predicting behavior, and developing self-configuring autonomic systems. However, incorporating ML into IIoT faces challenges such as high data complexity and variability, communication costs, lack of data privacy and security, and scalability. Federated multitask learning (FML) is a promising solution to tackle most of these challenges by training ML models locally and exchanging only the updated parameters. However, it still faces challenges in device and system compatibility, data heterogeneity, and scalability, especially for hierarchical heterogeneous IIoT environments. To address all these challenges, this article proposes a novel hybrid framework dubbed Clustered Multitask Federated Distillation (CMFD). In conjunction with the FML, CMFD combines clustered FL to address the data heterogeneity and knowledge distillation to address the system compatibility and scalability. To show the efficiency of CMFD for intelligent IIoT applications, we present a case study of heterogeneous IIoT environments using a realistic dataset. We conclude with useful insights emerging from our work and highlight promising future research directions.

Decentralized Energy Management Concept for Urban Charging Hubs With Multiple V2G Aggregators

This work introduces a decentralized management concept for the urban charging hubs (UCHs) where electric vehicles (EVs) can access multiple charger clusters, each controlled by an aggregator. The given day ahead schedules (DASs) and peak power limits (PPLs) of the aggregators providing grid-to-vehicle (G2V) and vehicle-to-grid (V2G) services can constrain the energy supply. A suitable energy management concept is required to prevent the impact of supply limitations on EV users. In the proposed approach, an electromobility operator (EMO) acting as an authorized entity, allocates incoming EVs into the charger clusters in the UCH. The EMO executes a smart routing (SR) algorithm that jointly optimizes the cluster allocations and charging schedules, minimizing the charging cost for the given dynamic price signals produced by the aggregators. For real-time charging control (RTC) of the charging units, each aggregator solves an optimization problem with periodically updated parameters given by the DAS/PPLs and charging commitments. This work demonstrates the effectiveness of the proposed concept through comparisons against benchmark strategies without SR and RTC. The results highlight that the proposed concept reduces the deviations from the DASs and the violations of PPLs while significantly decreasing unfulfilled charging demand and unscheduled discharge from EV batteries.

Novel method for modelling and adaptive estimation for SOC and SOH of lithium-ion batteries

Equivalent circuit models of lithium-ion batteries are the foundation of accurately estimating state of charge (SOC) and state of health (SOH). To further describe the performance of the batteries between working and idling states, we present for the first time the analysis of an improved fractional-order model (IFOM). Moreover, an endogenous immune algorithm (EIA) is proposed to identify the parameters of the model. Meanwhile, considering the aging effect, an adaptive dual square root Kalman filtering (ADSRCKF) with the dormancy zone is proposed to achieve the estimation. The first filter in ADSRCKF estimates SOC and SOH in which the SOH is characterised by the ohmic resistance. The second filter adaptively updates the drifted model parameters according to the dormancy zone. Finally, the proposed method is evaluated with the random walk charging and discharging test. The experimental results illustrate that the adaptively updated parameters can better match the battery status in real time, and the proposed ADSRCKF can achieve high accuracy with strong robustness. For the initial SOC error, the root mean square error (RMSE) of SOC is less than 0.3%, and the RMSE is approximately 1% even when the initial capacity is also deviated.