bluuebunny/crossref_metadata_2025_split huggingface dataset:
Ensemble machine learning based driving range estimation for real‐world electric city buses by considering battery degradation levels
Authors: Wang Yongxing , Lu Chaoru , Bi Jun , Sai Qiuyue , Zhang Yongzhi 
Published: April 2021

AbstractBattery electric buses (BEBs) have been regarded as effective options to address the congestion and pollution problems in the field of urban transportation. However, since the limited driving range of BEBs brings challenges for their promotion, the accurate estimation of the driving range with limited available information has become a critical issue for public transport operators. The real‐world data collected from 50 BEBs operated in two different cities is used to develop the driving range estimation method by considering the battery degradation effects. Firstly, the incremental capacity analysis method is introduced to characterize the battery performance, and the battery degradation levels under different charging modes are recognized. Afterward, four types of ensemble machine learning (EML) methods are adopted to model the driving range estimation. The BEB driving data, weather condition data and battery degradation levels are used to train and test the models with consideration of 17 impact factors together with two different charging modes. The results indicate that the ensemble machine learning methods have good performance overall, of which the random forest has the highest accuracy. Furthermore, the importance of influencing factors is analysed, and the relevant insights are discussed.
Co‐delivery of docetaxel and retinoic acid by poly (ethylene glycol)‐retinoic acid conjugates based micelles for synergistic prostate cancer therapy
Authors: Hu Hang , Xu Defeng 
Published: April 2021

AbstractIn the present work, docetaxel (DTX) and retinoic acid (RA) co‐loaded micelles were developed for synergistic prostate cancer therapy. PEG‐RA and RA‐PEG‐RA were synthesized and subsequently used for loading of DTX and RA. The as prepared DTX/RA@RA‐PEG‐RA micelles exhibit smaller size and higher drug loading capacity as compared to DTX/RA@PEG‐RA micelles. The in vitro drug release study shows that DTX/RA@PEG‐RA and DTX/RA@RA‐PEG‐RA micelles exhibit similar drug release behaviours in which DTX exhibits fast release and RA exhibits sustained release. Nile red (NR) was loaded into PEG‐RA and RA‐PEG‐RA micelles to investigate the cellular uptake of the micelles by fluorescence microscope and flow cytometry. The results show that NR@RA‐PEG‐RA micelles exhibit slightly enhanced cellular uptake as compared to NR@PEG‐RA micelles. The cellular uptake of DTX/RA@PEG‐RA and DTX/RA@RA‐PEG‐RA micelles determined by HPLC also show similar results. In in vitro cytotoxicity against C4‐2 and 22Rv1 cells, DTX in combination with RA exhibit synergistic antitumor effect, and DTX/RA@PEG‐RA and DTX/RA@RA‐PEG‐RA micelles exhibit enhanced cytotoxicity against C4‐2 cells and similar cytotoxicity against 22Rv1 cells as compared to DTX/RA. DTX/RA@PEG‐RA and DTX/RA@RA‐PEG‐RA micelles show significant potential for prostate cancer therapy.
Analysis on boundary conditions of soft switching for DC electric spring with parallel topology
Authors: Wang Qingsong , Zha Daojun , Cheng Ming , Deng Fujin , Buja Giuseppe 
Published: April 2021

AbstractThe concept of DC electric spring (DCES) has been introduced some years ago to solve the voltage stabilisation issue caused by the high penetration of intermittent renewable energy sources (RESs) into DC power systems. Recently, a featured DCES topology has been proposed, comprising an isolated three‐port DC/DC converter and an embedded energy storage system. The three ports are connected to a DC power system at the supply side, the critical load (CL) and the non‐critical load (NCL) at the user side, whilst the energy storage system is paralleled at the CL port. To improve the performance of the topology, this paper investigates the chance of making soft the switching of the DCES converters. After the DCES operation is analysed, equations describing the currents transients during switching have been worked out, from which the boundary conditions for zero‐voltage switching (ZVS) to occur are formulated and the resultant ZVS zone is illustrated with the help of visual graphs. The boundary conditions are examined to evaluate how the circuit parameters influence the extension of the ZVS zone. The examination shows that a suitable selection of the parameters widens the ZVS zone notably; its optimal extension is then found for a case study. The effectiveness of the ZVS findings for the featured DCES topology is validated by both simulation and experiment results.
A new family of boost active neutral point clamped inverter topology with reduced switch count
Authors: Siddique Marif Daula , Reddy B. Prathap , Iqbal Atif , Mekhilef Saad 
Published: April 2021

AbstractA new family of inverter topology for active neutral point clamped (ANPC) is proposed. The traditional ANPC gives the output voltage of half the input voltage which necessitates the use of higher input dc‐link voltage. To fix this problem, a floating capacitor is used to increase the voltage of the output, which decreases the dc‐link requirement. Moreover, no sensors are needed to stabilize the floating condenser for the proposed topology, which minimizes inverter complexities. The neutral point is connected directly to the load terminals, which decreases the leakage current and makes it suitable for solar PV integration. Two modes of operation of the proposed topology have been discussed while considering the voltage of the floating capacitor used in the topology. Furthermore, two extensions of the proposed topology with generalized structure have also been provided in the paper. A detailed comparison with similar topologies has been provided. The level‐shifted pulse width modulation technique has been used for the control of the proposed active neutral point clamped topology. In order to illustrate the idea of the proposed active neutral point clamped inverter, the theory of operation and analysis is accompanied by simulation, and experimental waveforms obtained from a laboratory prototype are provided.
Modified grey wolf optimisation based reduced device count 17‐level hybrid multilevel inverter
Authors: Routray Abhinandan , Singh Rajeev Kumar , Mahanty Ranjit 
Published: April 2021

AbstractA 17‐level hybrid multilevel inverter (HMLI) using modified grey wolf optimisation (MGWO) is proposed here. The proposed HMLI requires only a single dc source and a lesser number of circuit elements to obtain a multilevel voltage level. The proposed HMLI can also enhance the input voltage and has reverse current carrying capability. The selective harmonic elimination pulse width modulation technique is implemented through MGWO for determining the optimal switching angles of the proposed HMLI to eliminate lower order harmonics. The MGWO algorithm gives improved results as compared to other existing optimisation algorithms in terms of fitness value, convergence speed and harmonic profile of the output voltage. The proposed HMLI is also compared with some of the other existing HMLIs in terms of the number of circuit elements, peak inverse voltage and total blocking voltage to exhibit its advantages. Finally, a 550 W prototype is fabricated in the laboratory to evaluate the effectiveness of the proposed HMLI.
Model‐based control of four‐leg inverter for UPS applications considering the effect of neutral line inductor
Authors: Seyedalipour S. Sajjad , Shahparasti Mahdi , Hajizadeh Amin , Savaghebi Mehdi 
Published: April 2021

AbstractA control approach for three‐phase four‐leg LC‐filtered voltage source inverters (VSIs) is proposed for an uninterruptible power supply (UPS) application. A filter inductor in the fourth (neutral) leg of the VSI is considered to enhance the output voltage quality when feeding different types of loads in such a system. The proposed approach is based on the modelling of UPS VSI system in the dq0 synchronous frame. First, the switching functions of the inverter under study are extracted from the achieved system model. Afterward, the control approach is developed by introducing a virtual time constant, which simultaneously affects the system's damping ratio, and a virtual undamped natural frequency in the switching functions. It offers flexibility for improving the system dynamic and steady‐state responses as desired. While the final time constant decreases the transient response time and overshoot, the resulting undamped natural frequency leads to alleviation of steady‐state error in the output voltage. The performance of the proposed control approach is evaluated via SIMULINK‐based and real‐time simulation results.
State‐of‐charge estimation technique for lithium‐ion batteries by means of second‐order extended Kalman filter and equivalent circuit model: Great temperature robustness state‐of‐charge estimation
Authors: Fang Yanyan , Zhang Qiang , Zhang Hang , Xu Weicheng , Wang Linshu , Shen Xueling , Yun Fengling , Cui Yi , Wang Lin , Zhang Xin 
Published: April 2021

AbstractThe present work focuses on the state‐of‐charge (SOC) estimation of a lithium‐ion battery in terms of a second‐order extended Kalman filter (EKF). First, an equivalent circuit model is introduced to describe the performance of lithium‐ion batteries. The model parameters are then identified through hybrid pulse power characterization experiments conducted over a wide range of temperatures (−10 to 55°C). A two‐dimensional mathematical relationship is established with respect to the SOC and temperature based on a dual‐fifth polynomial expression. The main effects and sensitivities of the SOC and temperature on the parameters are analysed according to the principle of variance analysis and partial derivatives. An estimation algorithm is developed, which combines the two‐dimensional parameter model and second‐order EKF. Finally, the proposed approach is validated compared to other estimation schemes through discharge experiments under extreme temperatures and dynamic loading profiles, which yields experimental results that estimate the SOC with an absolute error of less than 4.5% under harsh conditions. This not only demonstrates that it can characterize dependency of the model parameters on the operating conditions and address the uncertainty of model parameters, but also verifies the advantage of present method at low temperatures especially at sub‐zero temperatures.
Data‐driven models for short‐term ocean wave power forecasting
Authors: Ni Chenhua 
Published: April 2021

AbstractIn order to integrate wave farms into the grid, the power from wave energy converters (WEC) must be forecasted. This study presents a novel data‐driven modelling (DDM) method to predict very short‐term (15 min–4 h) and short‐term (0–72 h) power generation from a WEC. The model is tested using data from an oscillating body converter. Several other methods are tested as well. These include support vector machines (SVM), neural networks (NN), and recurrent neural networks (RNN). Of these, the best is the long‐short‐term memory (LSTM) network, which is trained and updated on observed values. The experiments demonstrate both the SVM and NN forecast well. However, the proposed deep learning models predict them more accurately. The models work well over short horizons. At horizons longer than three days, accuracy deteriorates, and the models cannot fit the data well.
Modeling, simulation, and optimization of biogas‐diesel hybrid microgrid renewable energy system for electrification in rural area
Authors: Araoye Timothy Oluwaseun , Ashigwuike Evans Chinemezu , Egoigwe Sochima Vincent , Ilo Frederick Udebunu , Adeyemi Adeyinka Cornelius , Lawal Rasheed Segun 
Published: April 2021

AbstractThis research performed a techno‐economic analysis of diesel‐biogas hybrid microgrid system. The paper modeled, designed, and simulated the microgrid system using MATLAB/SIMULINK and performed system optimization using HOMER software. The anaerobic digestion (AD) processes were designed and simulated with the aid of Simulink to obtain the methane yield from the reactor. Results show that the methane yield is 95.04 kg/day at a reactor temperature of 55°C. The synchronous generator was modeled and simulated for the application of both diesel fuel and biogas fuel system. The HOMER software was used to optimize the hybrid micro‐grid system with the diesel system taken as the base case. Biogas production was varied between 1 and 5 tons while the calculated energy demand of the village was 271925 kWh. At a biomass production of 4 tons and above, the hybrid system became powered by only the biogas system for total energy production. The energy produced by biogas is 452820 kWh and a cost of energy (COE) of $0.0484. The net present cost (NPC) of the base case system is $1141292 while that of the hybrid system is $176600 and that of the biogas system is $170085 which shows the saving cost of 84.5% and 85.1%, respectively, compared to the base case system over the project lifetime.
Modelling battery energy storage systems for active network management—coordinated control design and validation
Authors: Parthasarathy Chethan , Sirviö Katja , Hafezi Hossein , Laaksonen Hannu 
Published: April 2021

AbstractControl of battery energy storage systems (BESS) for active network management (ANM) should be done in coordinated way considering management of different BESS components like battery cells and inverter interface concurrently. In this paper, a detailed and accurate lithium‐ion battery model has been used to design BESS controls, thereby allowing improved overall power system control design optimisation studies by simultaneously considering both component and system‐level aspects. This model is utilised to develop a multi‐objective ANM scheme (a) to enhance utilisation of wind power generation locally by means of active power (P)‐ control of BESSs; (b) to utilise distributed energy resources (i.e. BESS and wind turbine generators) to maintain system voltage within the limits of grid code requirements by reactive power/voltage (QU)‐ and active power/voltage (PU)‐ controls. BESS control strategies to implement the ANM scheme, are designed and validated through real‐time simulation in an existing smart grid pilot, Sundom Smart Grid (SSG), in Vaasa, Finland.
A dummy peak elimination based MPPT technique for PV generation under partial shading condition
Authors: Sai Boni Satya Varun , Khadtare Sarang A. , Chatterjee Debashis 
Published: April 2021

AbstractThe increased energy demand with recent advancement in technology has led the global energy producers to move towards solar energy. Partial shading is a phenomenon that can severely affect the production of the solar power in the photovoltaic system. With partial shading situation, the cascaded solar panels show numerous dummy peaks both in power–voltage (P–V) and current–voltage (I–V) curves, resulting in difficulty of extracting maximum power. A dummy peak elimination based MPPT technique is introduced in proposed work for accurate tracking of maximum power. The proposed dummy peak elimination based MPPT estimates the dummy peaks without imposing any high computational burden for the processor. The proposed technique has better dynamic response and is more compatible for real time application compared to existing optimization‐based techniques. The proposed dummy peak elimination based MPPT is tested on a practical system with 3S configuration having three 249 W PV panels for different shading patterns, which displays acceptable outputs with good dynamic performance and better efficiency.
Risk‐based optimal operation of coordinated natural gas and reconfigurable electrical networks with integrated energy hubs
Authors: Hemmati Mohammad , Abapour Mehdi , Mohammadi‐Ivatloo Behnam , Anvari‐Moghaddam Amjad 
Published: April 2021

AbstractThis paper elaborates on optimal scheduling of coordinated power and natural gas (NG) networks in the presence of interconnected energy hubs considering reconfiguration as a flexibility source. With regard to the energy hub system consisting of several generation units, storage and conversion technologies, as well as natural gas‐fired units, the high interdependency between gas and electricity carriers should be captured. The hourly reconfiguration capability is developed for the first time in a multi‐energy system to enhance the optimal power dispatch and gas consumption pattern. The realistic interdependency of electrical and NG grids is investigated by employing the steady‐state Weymouth equation and AC‐power flow model for power and gas networks, respectively. Furthermore, to handle the risk associated with strong uncertainty of wind power, load, and real‐time power price, the conditional value at risk approach is employed. The proposed model is implemented on the integrated test system and simulation results are presented for different cases. The impact of the risk aversion level on operating cost and optimal scheduling of controllable units is examined. Numerical results demonstrate that reconfigurable capability reduces the operational cost up to 7.82%.
A benchmark of dynamic versus static methods for facial action unit detection
Authors: Alharbawee L. , Pugeault N. 
Published: April 2021

AbstractAction Units activation is a set of local individual facial muscle parts that occur in time constituting a natural facial expression event. AUs occurrence activation detection can be inferred as temporally consecutive evolving movements of these parts. Detecting AUs automatically can provide explicit benefits since it considers both static and dynamic facial features. Our work is divided into three contributions: first, we extracted the features from Local Binary Patterns, Local Phase Quantisation, and dynamic texture descriptor LPQTOP with two distinct leveraged network models from different CNN architectures for local deep visual learning for AU image analysis. Second, cascading the LPQTOP feature vector with Long Short‐Term Memory is used for coding longer term temporal information. Next, we discovered the importance of stacking LSTM on top of CNN for learning temporal information in combining the spatially and temporally schemes simultaneously. Also, we hypothesised that using an unsupervised Slow Feature Analysis method is able to leach invariant information from dynamic textures. Third, we compared continuous scoring predictions between LPQTOP and SVM, LPQTOP with LSTM, and AlexNet. A competitive substantial performance evaluation was carried out on the Enhanced CK dataset. Overall, the results indicate that CNN is very promising and surpassed all other methods
Coal breakage features and fragment size distribution in water jet drilling for coalbed methane development
Authors: Xiao Songqiang , Ren Qingyang , Wang Haiyang , Liu Jialiang 
Published: April 2021

AbstractWater jet drilling (WJD) technology has been widely used to extract coalbed methane. Coal breakage features and fragment size in WJD determine the drilling efficiency. The smaller the coal fragments, the easier the fragments are discharged from hydraulic boreholes. This study discloses the coal breakage features and size distributions of fragments, field experiments under the original in‐situ stress condition were conducted to obtain the coal fragments generated in WJD. The effects of the jet pressure and coal strength on fragment size distributions were investigated based on the Weibull distribution and fractal model. The results indicate that fine particles dominate the coal fragments and its proportion increases with the increase of jet pressure and the decrease of coal strength. The fragment size distributions accord with the Weibull distribution. The breakage degree index of fragments decreases in the logarithmic form as the jet pressure increases. The coal fragments have the fractal characteristic, and the fractal dimension increases with the increasing jet pressure and decreases with the increasing coal strength. Additionally, the fractal dimension increases with the decreases in breakage degree index. This work could provide a guide for research on the coal fragments transportation and the selections of hydraulic parameters in WJD.
Correction of atmospheric stray light in restored slit spectra
Authors: Saranathan S. , van Noort M. , Solanki S. K. 
Published: April 2021

Context. A long-standing issue in solar ground-based observations has been the contamination of data due to stray light, which is particularly relevant in inversions of spectropolarimetric data.
Aims. We aim to build on a statistical method of correcting stray-light contamination due to residual high-order aberrations and apply it to ground-based slit spectra.
Methods. The observations were obtained at the Swedish Solar Telescope, and restored using the multi-frame blind deconvolution restoration procedure. Using the statistical properties of seeing, we created artificially degraded synthetic images generated from magneto-hydrodynamic simulations. We then compared the synthetic data with the observations to derive estimates of the amount of the residual stray light in the observations. In the final step, the slit spectra were deconvolved with a stray-light point spread function to remove the residual stray light from the observations.
Results. The RMS granulation contrasts of the deconvolved spectra were found to increase to approximately 12.5%, from 9%. Spectral lines, on average, were found to become deeper in the granules and shallower in the inter-granular lanes, indicating systematic changes to gradients in temperature. The deconvolution was also found to increase the redshifts and blueshifts of spectral lines, suggesting that the velocities of granulation in the solar photosphere are higher than had previously been observed.