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Abstract In the forthcoming decades, significant advancements will shape the construction and operations of distribution systems. Particularly, the increasing prominence of photovoltaic (PV) systems in the power industry will impact the security of these systems. This study employs Monte Carlo Simulation (MCS) in conjunction with genetic algorithm (GA) and differential evolution (DE) to address uncertainties. The probability density functions (pdf) for total voltage harmonic distortion (UTHD), individual voltage harmonic distortion (UIHDh), and RMS voltage (URMS) have been determined for utilization in chance constrained framework. In addition, the uncertainty effects of PV systems on grid losses for various solar radiation conditions are also investigated. Specifically, the paper aims to evaluate these impacts within the context of stochastic limits. The PV system sizing problem has been addressed inside the distribution system using a chance-constrained framework. A key contribution is the integration of GA, DE, and MCS into a cohesive approach, and the study evaluates the benefits of this approach through an analysis of outcomes derived from the stochastic method. The simulation results illustrate the advantages of the proposed stochastic GA methodology.

Abstract Research on Reconfigurable Battery Systems (RBS) is gaining emphasis over the traditional fixed topology of the battery pack due to its advantages of adapting flexible topology (series‐parallel) during its operation in the pack for meeting the non‐linear time‐dependent load requirements. There could emerge serious issues such as those related to safety due to malfunction of the switching circuit, heat generation from switches during frequent switching of circuits, charging temperature rise, increased charging time, sensing issues arising from the use of low‐accuracy voltage/current sensors, state of charge/state of health estimation, and cost issues due to the use of increasing number of switches, fuses, contactors, relays, circuit breakers etc. To address these mentioned issues, the problem of optimal switching circuit topology for RBS is formulated as a mathematical multi‐objective optimisation problem. An intelligent system framework based on digital twins is proposed. The proposed framework is further extended to a life cycle management approach that includes the interactions among pack design, pack assembly and operational and recycling levels. This could provide greater access of real‐time big data cloud storage to the battery designers, manufacturers and recycling industries, who can make use of it to optimise their designs, systems and operations.

Objectives: Staphylococcus aureus bacteremia (SAB) is a common infection worldwide. We compared SAB mortality in low- and middle-income countries (LMIC) versus high-income countries (HIC) in a meta-analysis. Methods: We searched MEDLINE, Embase, and Cochrane Database of Systematic Reviews from 1991-2021 and included observational, single-country studies on patients with positive blood cultures for S. aureus. The main outcome was the proportion of patients with SAB who died in the hospital. A generalized linear mixed random-effects model was used to pool estimates, and a meta-regression was used to adjust for study-level characteristics. Results: A total of 332 studies involving 517,671 patients in 39 countries were included. No study was conducted in a low-income country. Only 33 (10%) studies were performed in middle-income countries (MIC), which described 6,216 patients. The pooled in-hospital mortality was 32.4% (95% confidence interval [CI] 27.2%-38.2%, T2 = 0.3063) in MIC and 22.3% (95% CI 20.1%-24.6%, T2 = 0.3257) in HIC. In a meta-regression model, MIC had higher in-hospital mortality (adjusted odds ratio 1.37, 95% CI 1.11-1.71; P = 0.0042) than HIC. Conclusion: In SAB studies, LMIC are poorly represented. In-hospital mortality was significantly higher in MIC than in HIC. Research should be conducted in LMIC to characterize differences in care processes driving the mortality gap.

We propose and investigate a queueing model of a battery swapping and charging station (BSCS) for electric vehicles (EVs). A new approach to the analysis of the queueing model is developed, which combines the representation of the model as a stochastic dynamic system with the use of the methods and results of tropical algebra, which deals with the theory and applications of algebraic systems with idempotent operations. We describe the dynamics of the queueing model by a system of recurrence equations that involve random variables (RVs) to represent the interarrival time of incoming EVs. A performance measure for the model is defined as the mean operation cycle time of the station. Furthermore, the system of equations is represented in terms of the tropical algebra in vector form as an implicit linear state dynamic equation. The performance measure takes on the meaning of the mean growth rate of the state vector (the Lyapunov exponent) of the dynamic system. By applying a solution technique of vector equations in tropical algebra, the implicit equation is transformed into an explicit one with a state transition matrix with random entries. The evaluation of the Lyapunov exponent reduces to finding the limit of the expected value of norms of tropical matrix products. This limit is then obtained using results from the tropical spectral theory of deterministic and random matrices. With this approach, we derive a new exact formula for the mean cycle time of the BSCS, which is given in terms of the expected value of the RVs involved. We present the results of the Monte Carlo simulation of the BSCS’s operation, which show a good agreement with the exact solution. The application of the obtained solution to evaluate the performance of one BSCS and to find the optimal distribution of battery packs between stations in a network of BSCSs is discussed. The solution may be of interest in the case when the details of the underlying probability distributions are difficult to determine and, thus, serves to complement and supplement other modeling techniques with the need to fix a distribution.

Abstract Lithium‐ion batteries have become a core component of electric vehicles (EVs) because of their high energy density. However, several issues in lithium‐ion batteries usage, such as safety, durability, charging time, and driving range, limit the development of EVs. Meanwhile, with the emergence of Industry 4.0, the digital twins technology has received widespread attention in the manufacturing industry because it provides real‐time monitoring and intelligent management of the production process. The authors propose a framework based on digital twins, which can be used for real‐time monitoring, intelligent management, and autonomous control of battery packs. The framework covers all aspects of a battery pack's lifecycle, including design, manufacturing, operation monitoring, and second use options. Such a framework can solve some critical issues inhibiting the usage of batteries. A case study of the application of the proposed digital twins‐based framework to electric vehicle battery systems has been conducted. The results show that deploying digital twins into the battery packs of EVs will improve the safety and service life of the battery packs.

Efficient fast-charging technology is necessary for the extension of the driving range of electric vehicles. However, lithium-ion cells generate immense heat at high-current charging rates. In order to address this problem, an efficient fast charging–cooling scheduling method is urgently needed. In this study, a liquid cooling-based thermal management system equipped with mini-channels was designed for the fast-charging process of a lithium-ion battery module. A neural network-based regression model was proposed based on 81 sets of experimental data, which consisted of three sub-models and considered three outputs: maximum temperature, temperature standard deviation, and energy consumption. Each sub-model had a desirable testing accuracy (99.353%, 97.332%, and 98.381%) after training. The regression model was employed to predict all three outputs among a full dataset, which combined different charging current rates (0.5C, 1C, 1.5C, 2C, and 2.5C (1C = 5 A)) at three different charging stages, and a range of coolant rates (0.0006, 0.0012, and 0.0018 kg·s−1). An optimal charging–cooling schedule was selected from the predicted dataset and was validated by the experiments. The results indicated that the battery module’s state of charge value increased by 0.5 after 15 min, with an energy consumption lower than 0.02 J. The maximum temperature and temperature standard deviation could be controlled within 33.35 and 0.8 °C, respectively. The approach described herein can be used by the electric vehicles industry in real fast-charging conditions. Moreover, optimal fast charging–cooling schedule can be predicted based on the experimental data obtained, that in turn, can significantly improve the efficiency of the charging process design as well as control energy consumption during cooling.

The heat dissipation capability of the battery thermal management system (BTMS) is a prerequisite for the safe and normal work of the battery. Currently, many researchers have designed and studied the structure of BTMS to better control the battery temperature in a specific range and to obtain better temperature uniformity. This allows the battery to work safely and efficiently while extending its life. As a result, BTMS has been a hot topic of research. This work investigates the impact of pin-fins on the heat dissipation capability of the BTMS using the computational fluid dynamics (CFD) approach, designs several BTMS schemes with different pin-fin structures, simulates all schemes for fluid-structure interaction, and examines the impact of different distribution, number, and shape of pin-fins on heat dissipation capability and pressure drop. Analyzing the effect of cooling plates with different pin-fins on the thermal capability of the BTMS can provide a basis for the structural design of this BTMS with pin-fin cooling plates. The findings demonstrate that the distribution and quantity of pin-fin shapes might affect heat dissipation. The square-section pin-fins offer better heat dissipation than other pin-fin shapes. As the pin-fins number increases, the maximum battery temperature decreases, but the pressure drop increases. It has been observed that uniform pin-fin distribution has a superior heat dissipation effect than other pin-fin distribution schemes. In summary, the cooling plate with a uniform distribution of 3 × 6 square section pin-fins has better heat dissipation capability and less power consumption, with a maximum battery temperature of 306.19 K, an average temperature of 304.20 K, a temperature difference of 5.18 K, and a pressure drop of 99.29 Pa.

Permanent magnet synchronous motors (PMSM) have been substantially used in electric vehicles (EVs) due to their advantages such as low loss, large torque, and high power density. With the continuous improvement of the PMSM performance requirements, its heat dissipation has also attracted increasing attention. This paper proposes a cooling system to realize the heat dissipation of the motor through internal oil circulation and external water circulation. Meanwhile, to obtain the best cooling system parameters, an optimization framework is developed for heat dissipation optimization of the motor. First, the most suitable Latin hypercube sampling (LHS) method is selected for sampling through the Coordinates exchange algorithm. Second, we separately study the modeling accuracy of thirteen surrogate models and finally select the back propagation (BP) neural network model. Then, we use six multi-objective optimization algorithms (MOOAs) to optimize the model, and select the optimal solution via the utopian point method. Finally, the motor heat dissipation situation is effectively improved, and the effectiveness and reliability of the optimization framework are proved, which provides an alternative mean for the heat dissipation design optimization of the motor and has prominent practical significance.

An energy-storage system comprised of lithium-ion battery modules is considered to be a core component of new energy vehicles, as it provides the main power source for the transmission system. However, manufacturing defects in battery modules lead to variations in performance among the cells used in series or parallel configuration. This variation results in incomplete charge and discharge of batteries and non-uniform temperature distribution, which further lead to reduction of cycle life and battery capacity over time. To solve this problem, this work uses experimental and numerical methods to conduct a comprehensive investigation on the clustering of battery cells with similar performance in order to produce a battery module with improved electrochemical performance. Experiments were first performed by dismantling battery modules for the measurement of performance parameters. The k-means clustering and support vector clustering (SVC) algorithms were then employed to produce battery modules composed of 12 cells each. Experimental verification of the results obtained from the clustering analysis was performed by measuring the temperature rise in the cells over a certain period, while air cooling was provided. It was found that the SVC-clustered battery module in Category 3 exhibited the best performance, with a maximum observed temperature of 32 °C. By contrast, the maximum observed temperatures of the other battery modules were higher, at 40 °C for Category 1 (manufacturer), 36 °C for Category 2 (manufacturer), and 35 °C for Category 4 (k-means-clustered battery module). Keywords: Clustering algorithm, Battery module, Equalization, Electric vehicle

Abstract The application of lithium‐ion batteries especially for electric vehicles has been limited by the factors of safety, lifetime, charging time, and cost. One of the principal limitations is that the performance of Li‐ion batteries drops intensely in a cold environment. Cold environment dramatically reduces the available capacity of the batteries and increases its internal impedance at the same time. Therefore, the estimation of state‐of‐health is of great importance in battery performance evaluation and lifetime prediction. Furthermore, the heating methods need to be developed to ensure that batteries work in abnormal temperature conditions. This paper conducts a comprehensive review specifically on the poor performance of lithium‐ion cells under severe conditions. The content contains three sections. First, a comprehensive study on the aging mechanisms of lithium‐ion batteries at cold temperatures is undertaken. Second, the estimation methods of the health state of the batteries are conducted, which is vital to understand the fundamentals and quantify the performance and aging effects for lithium‐ion batteries. Third, the heating methods are classified and studied in detail to reduce the degradation mechanism and promote the performance of lithium‐ion batteries under sub‐zero conditions.

Acute hypotensive episode (AHE) is one of the postoperative complications with the high sudden death rate in the intensive care unit (ICU), which threatens patients' life safety seriously. Therefore, it is meaningful, valuable, and essential for the analysis including the prediction of AHE before its actual occurrence. In this paper, a novel methodology combining with deep network and multiple-gene expression programming is presented to predict the AHE automatically which will improve the efficiency of prevention and lighten the burden of diagnosis for ICU doctors. The experimental data (time series data) are obtained from multi-intelligent monitoring in intensive care II (MIMICII). The sliding window (SW) is used to generate the data stream segments from the original sources. A decomposition method (ensemble empirical mode decomposition (EEMD)) for highly complex and the nonlinear signal process is used to decompose each data stream segments into a group of intrinsic mode functions (IMFs). A novel three-layer auto-encoder network is then presented for the learning and extracting of features automatically in the mean arterial pressure (MAP) time series integrated with the SW, EEMD, and deep learning (DL) techniques. At last, the feature sets are used to construct classification models using multiple gene expression programming (GEP) classifier. The result shows that the proposed method has higher prediction accuracy of 86.16% and robustness in the prediction of the AHE.

The explosion of electric vehicles (EVs) has triggered massive growth in power lithium-ion batteries (LIBs). The primary issue that follows is how to dispose of such large-scale retired LIBs. The echelon utilization of retired LIBs is gradually occupying a research hotspot. Solving the issue of echelon utilization of large-scale retired power LIBs brings not only huge economic but also produces rich environmental benefits. This study systematically examines the current challenges of the cascade utilization of retired power LIBs and prospectively points out broad prospects. Firstly, the treatments of retired power LIBs are introduced, and the performance evaluation methods and sorting and regrouping methods of retired power LIBs are comprehensively reviewed for echelon utilization. Then, the problems faced by the scenario planning and economic research of the echelon utilization of retired power LIBs are analyzed, and value propositions are put forward. Secondly, this study summarizes the technical challenges faced by echelon utilization in terms of security, performance evaluation methods, supply and demand chain construction, regulations, and certifications. Finally, the future research prospects of echelon utilization are discussed. In the foreseeable future, technologies such as standardization, cloud technology, and blockchain are urgently needed to maximize the industrialization of the echelon utilization of retired power LIBs.

This research aims to evaluate the influence of different 3D metals (Fe, Co, and Ni) substituted to Mn on the electrochemical performance of P2-NaxMe1/3Mn2/3O2 material, which was synthesized by the coprecipitation process followed by calcination at high temperature. X-ray diffraction (XRD) results revealed that the synthesized Mn-rich materials possessed a P2-type structure with a negligible amount of oxide impurities. The materials possessed their typical cyclic voltammogram and charge-discharge profiles; indeed, a high reversible redox reaction was obtained by NaxCo1/3Mn2/3O2 sample. Both NaxCo1/3Mn2/3O2 and NaxFe1/3Mn2/3O2 provided a high specific capacity of above 140 mAh·g−1; however, the former showed better cycling performance with 83% capacity retention after 50 cycles at C/10 and high rate capability. Meanwhile, the Ni-sub NaxNi1/3Mn2/3O2 exhibited excellent cycling stability but a low specific capacity of 110 mAh·g−1 and inferior rate capability. The diffusion coefficient of Na+ ions into the structure tended to decrease with a depth of discharge; those values were in the range of 10−10–10−9 cm2·s−1 and 10−11–10−10 cm2·s−1 in the solid solution region and biphasic region, respectively.

An effective battery thermal management system (BTMS) is essential to ensure that the battery pack operates within the normal temperature range, especially for multi-cell batteries. This paper studied the optimal configuration of an air-cooling (AC) system for a cylindrical battery pack. The thermal parameters of the single battery were measured experimentally. The heat dissipation performance of a single battery was analyzed and compared with the simulation results. The experimental and simulation results were in good agreement, which proves the validity of the computational fluid dynamics (CFD) model. Various schemes with different battery arrangements, different positions of the inlet and outlet of the cooling system and the number of inlets and outlets were compared. The results showed that an arrangement that uses a small length-width ratio is more conducive to promoting the performance of the cooling system. The inlet and outlet configuration of the cooling system, which facilitates fluid flow over most of the battery pack over shorter distances is more beneficial to battery thermal management. The configuration of a large number of inlets and outlets can facilitate more flexible adjustment of the fluid flow state and can slow down battery heating to a greater extent.

This chapter presents a reliability study for an offshore jacket structure with emphasis on the features of nonconventional modeling. Firstly, a random set model is formulated for modeling the random waves in an ocean site. Then, a jacket structure is investigated in a pushover analysis to identify the critical wave direction and key structural elements. This is based on the ultimate base shear strength. The selected probabilistic models are adopted for the important structural members and the wave direction is specified in the weakest direction of the structure for a conservative safety analysis. The wave height model is processed in a P-box format when it is used in the numerical analysis. The models are applied to find the bounds of the failure probabilities for the jacket structure. The propagation of this wave model to the uncertainty in results is investigated in both an interval analysis and Monte Carlo simulation. The results are compared in context of information content and numerical accuracy. Further, the failure probability bounds are compared with the conventional probabilistic approach.

The present methods of diagnosing depression are entirely dependent on self-report ratings or clinical interviews. Those traditional methods are subjective, where the individual may or may not be answering genuinely to questions. In this paper, the data has been collected using self-report ratings and also using electronic smartwatches. This study aims to develop a weighted average ensemble machine learning model to predict major depressive disorder (MDD) with superior accuracy. The data has been pre-processed and the essential features have been selected using a correlation-based feature selection method. With the selected features, machine learning approaches such as Logistic Regression, Random Forest, and the proposed Weighted Average Ensemble Model are applied. Further, for assessing the performance of the proposed model, the Area under the Receiver Optimization Characteristic Curves has been used. The results demonstrate that the proposed Weighted Average Ensemble model performs with better accuracy than the Logistic Regression and the Random Forest approaches.

Magnesium alloys have found widespread applications in diverse fields such as aerospace, automotive, bio-medical and electronics industries due to its relatively high strength-to-weight ratio. However, stress corrosion cracking of these alloys severely restricts their applications in several novel technologies. Hence, it will be useful to identify the corrosion mechanics of magnesium alloys under external stresses as it can provide further insights on design of these alloys for critical applications. In the present study, the corrosion mechanics of a commonly used magnesium alloy, AZ31, is studied using finite element simulation with a modified constitutive material damage model. The data obtained from the finite element modeling were further used to formulate a mathematical model using computational intelligence algorithm. Sensitivity and parametric analysis of the derived model further corroborated the mechanical response of the alloy in line with the corrosion physics. The proposed approach is anticipated to be useful for materials engineers for optimizing the design criteria for magnesium alloys catered for high temperature applications.

Background. Four-node sampling is a useful substitute for sentinel node biopsy in low resource settings. USG is being increasingly used as a preoperative tool to evaluate axilla. We conducted this study to assess the accuracy of different descriptors of axillary ultrasound and to formulate a model on grading of axillary involvement. Material and Methods. Thirty-four patients with clinically negative axilla underwent preoperative axillary ultrasound. The suspicious nodes were marked and details of various descriptors were noted. These nodes were sampled during axillary dissection and correlation of ultrasonographic findings with histopathological report was done to calculate accuracy of different descriptors. Based on this, a grading system of axillary lymph nodes involvement was formulated. Results. Based on the presence of various descriptors, five grades of nodal involvement could be defined. The most accurate descriptors to indicate nodal involvement were loss of hilar fat and hypoechoic internal echoes with specificity of 83% and positive predictive value of 92% each. The combination of descriptors of round shape with loss of hilar fat and hypoechoic internal echos had 100% specificity and positive predictive value. Conclusions. Grading of nodal involvement on axillary USG can be useful for selecting the most suspicious nodes for sampling during axillary dissection.