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In this paper, three typical permanent magnet machines for All-Electric aircraft propulsion are designed, optimized and compared. Firstly, the necessary performances are determined based on the requirements of high power/torque density and an aircraft with a maximum takeoff weight of 1500 kg. The initial structures of interior permanent magnet (IPM) synchronous machine, vernier permanent magnet (VPM) machine and flux-switching permanent magnet (FSPM) machine are designed with identical stator outer radius and rotor shaft length. Then, parametric sensitivity analysis and multi-objective particle swarm are combined as an optimization methodology to optimize these machines. Based on the finite element analysis, the electromagnetic performances such as no-load airgap flux density harmonic spectrum, cogging torque, average output torque, losses, efficiency and power factor, etc., are generally compared and analyzed. Subsequently, the discussions are carried out, where the strength and weakness of these three machines are concluded and the future prospects are suggested. Finally, it is concluded that if effective measures are implemented to reduce the permanent magnet eddy current loss and improve the power factor, the VPM machine would be the most suitable choice among these machines for All-Electric aircraft propulsion. [ABSTRACT FROM AUTHOR]

Discrete fracture network (DFN) is an effective means of describing the coupling of heat flow in an underground fractured rock mass. In this paper, an improved DFN is proposed by introducing the correlation function of fracture trace length and aperture, which is more consistent with the real fracture data. Next, based on the improved model, the influence of fracture roughness on the fluid flow and heat transmission was evaluated, and the relationship between the fracture aperture and the joint roughness coefficient (JRC) is established. Finally, based on the exponential function between confining pressure and aperture, the influence of confining pressure on the heat‐flow coupling process is considered in the improved model. Besides, the reliability of the model was verified by comparing with the analytical solution of the two‐dimensional single‐fracture heat‐flow coupling problem. The results show that under the same conditions, considering the correlation between the geometric parameters of the fracture, the seepage and heat transfer rates of the model increased, the outlet boundary flow reached the maximum value, and the average outlet temperature dropped rapidly. However, the fracture roughness reduces the outlet flow rate and the decline rate of average temperature. The confining pressure will lead to a decrease of about 3.5% in the outlet flow of the model, which is consistent with the seepage law in practical engineering. The model presented in this paper is an effective supplement to the two‐dimensional fracture network heat‐flow coupling model and can provide a theoretical basis and a numerical calculation tool for related underground rock mass engineering. [ABSTRACT FROM AUTHOR]

An accurate state of charge (SOC) can provide effective judgment for the BMS, which is conducive for prolonging battery life and protecting the working state of the entire battery pack. In this study, the first-order RC battery model is used as the research object and two parameter identification methods based on the least square method (RLS) are analyzed and discussed in detail. The simulation results show that the model parameters identified under the Federal Urban Driving Schedule (HPPC) condition are not suitable for the Federal Urban Driving Schedule (FUDS) condition. The parameters of the model are not universal through the HPPC condition. A multitimescale prediction model is also proposed to estimate the SOC of the battery. That is, the extended Kalman filter (EKF) is adopted to update the model parameters and the adaptive unscented Kalman filter (AUKF) is used to predict the battery SOC. The experimental results at different temperatures show that the EKF-AUKF method is superior to other methods. The algorithm is simulated and verified under different initial SOC errors. In the whole FUDS operating condition, the RSME of the SOC is within 1%, and that of the voltage is within 0.01 V. It indicates that the proposed algorithm can obtain accurate estimation results and has strong robustness. Moreover, the simulation results after adding noise errors to the current and voltage values reveal that the algorithm can eliminate the sensor accuracy effect to a certain extent. [ABSTRACT FROM AUTHOR]

Connectivity is an effective indicator to describe the flow paths and mechanical behavior of reservoir fractured rock masses. The connectivity evaluation of fracture networks plays an important role in the design, assessment, and development of reservoirs in several important engineering applications, such as geothermal recovery and petroleum industry. Based on Allard's definition of a connectivity index, a 3D connectivity evaluation method is proposed in this study by building the connectivity functions of the supports. Meanwhile, different connectivity functions are built for different situations. In addition, a convex polygon is used to simulate the fracture to make the fracture network as real as possible. The accuracy of the generated fracture network was verified by comparing three parameters of fracture, and the results showed that the relative error was less than 5%. Some examples in the Ordos Basin, China, are given to verify the correctness of the program and method. Analysis results show that the 3D method is more realistic than the 2D connectivity evaluation, and the connectivity field of the fracture network is more reliable in the 3D method. Moreover, the proposed method can show the potential preferential flow path in a stereoscopic manner and thus greatly extends the function of connectivity analysis. This method provides important information for the prediction of paths and the evaluation of the connectivity of the fracture network and is important in evaluating the optimal location of geothermal recovery and petroleum industry. [ABSTRACT FROM AUTHOR]

The battery monitoring system (BMS) plays a crucial role in maintaining the safe operation of the lithium battery electric bicycle and prolonging the life of the battery pack. This paper designed a set of new battery monitoring systems based on the Android system and ARM single-chip microcomputer to enable direct management of the lithium battery pack and convenient monitoring of the state of the battery pack. The BMS realizes the goal of monitoring the voltage, current, and ambient temperature of lithium batteries, estimating the state of charge (SOC) and state of health (SOH), protecting the battery from abuse during charging or discharging, and ensuring the consistency of the batteries by integrating the passive equalization circuit. The BMS was proven effective and feasible through several tests, including charging/discharging, estimation accuracy, and communication tests. The results indicated that the BMS could be used in the design and application of the electric bicycle. [ABSTRACT FROM AUTHOR]

SUMMARY An element-free Galerkin (EFG) method with linear, quadratic and cubic approximations, which can exactly, in a numerical sense, pass the corresponding patch tests is proposed and is named as consistent EFG (CEFG) method. The development of this method is based on the Hu-Washizu three-field variational principle. Numerical integration schemes with corrected nodal derivatives at quadrature points are proposed according to the satisfaction of the orthogonality condition between stress and strain difference. Thus, the method is variationally consistent. The consistency of the corrected nodal derivatives and the satisfaction of patch test conditions are theoretically proved and also numerically validated. Numerical results show that the proposed CEFG method greatly improves the numerical performance of the EFG method in terms of accuracy, convergence, efficiency and stability, especially the proposed cubic CEFG method, which shows exceptional accuracy and convergence. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

An accurate state-of-charge (SOC) can not only provide a safe and reliable guarantee for the entirety of equipment but also extend the service life of the battery pack. Given that the chemical reaction inside the lithium-ion battery is a highly nonlinear dynamic system, obtaining an accurate SOC for the battery management system is very challenging. This paper proposed a gated recurrent unit recurrent neural network model with activation function layers (GRU-ATL) to estimate battery SOC. The model used deep learning technology to establish the nonlinear relationship between current, voltage, and temperature measurement signals and battery SOC. Then the online SOC estimation was carried out on different testing sets using the trained model. The experiments in this paper showed that the GRU-ATL network model could realize online SOC estimation under different working conditions without relying on an accurate battery model. Compared with the gated recurrent unit recurrent neural (GRU) network model and long short-term memory (LSTM) network model, the GRU-ATL network model had more stable and accurate SOC prediction performance. When the measurement data contained noise, the experimental results showed that the SOC prediction accuracy of GRU-ATL model was 0.1–0.4% higher than the GRU model and 0.3–0.7% higher than the LSTM model. The mean absolute error (MAE) of SOC predicted by the GRU-ATL model was stable in the range of 0.7–1.4%, and root mean square error (RMSE) was stable between 1.2–1.9%. The model still had high prediction accuracy and robustness, which could meet the SOC estimation in complex vehicle working conditions. [ABSTRACT FROM AUTHOR]

Vehicle detection is an indispensable part of environmental perception technology for smart cars. Aiming at the issues that conventional vehicle detection can be easily restricted by environmental conditions and cannot have accuracy and real-time performance, this article proposes a front vehicle detection algorithm for smart car based on improved SSD model. Single shot multibox detector (SSD) is one of the current mainstream object detection frameworks based on deep learning. This work first briefly introduces the SSD network model and analyzes and summarizes its problems and shortcomings in vehicle detection. Then, targeted improvements are performed to the SSD network model, including major advancements to the basic structure of the SSD model, the use of weighted mask in network training, and enhancement to the loss function. Finally, vehicle detection experiments are carried out on the basis of the KITTI vision benchmark suite and self-made vehicle dataset to observe the algorithm performance in different complicated environments and weather conditions. The test results based on the KITTI dataset show that the mAP value reaches 92.18%, and the average processing time per frame is 15 ms. Compared with the existing deep learning-based detection methods, the proposed algorithm can obtain accuracy and real-time performance simultaneously. Meanwhile, the algorithm has excellent robustness and environmental adaptability for complicated traffic environments and anti-jamming capabilities for bad weather conditions. These factors are of great significance to ensure the accurate and efficient operation of smart cars in real traffic scenarios and are beneficial to vastly reduce the incidence of traffic accidents and fully protect people's lives and property. [ABSTRACT FROM AUTHOR]

Pedestrian detection is an important aspect of the development of intelligent vehicles. To address problems in which traditional pedestrian detection is susceptible to environmental factors and are unable to meet the requirements of accuracy in real time, this study proposes a pedestrian detection algorithm for intelligent vehicles in complex scenarios. YOLOv3 is one of the deep learning-based object detection algorithms with good performance at present. In this article, the basic principle of YOLOv3 is elaborated and analyzed firstly to determine its limitations in pedestrian detection. Then, on the basis of the original YOLOv3 network model, many improvements are made, including modifying grid cell size, adopting improved k-means clustering algorithm, improving multi-scale bounding box prediction based on receptive field, and using Soft-NMS algorithm. Finally, based on INRIA person and PASCAL VOC 2012 datasets, pedestrian detection experiments are conducted to test the performance of the algorithm in various complex scenarios. The experimental results show that the mean Average Precision (mAP) value reaches 90.42%, and the average processing time of each frame is 9.6 ms. Compared with other detection algorithms, the proposed algorithm exhibits accuracy and real-time performance together, good robustness and anti-interference ability in complex scenarios, strong generalization ability, high network stability, and detection accuracy and detection speed have been markedly improved. Such improvements are significant in protecting the road safety of pedestrians and reducing traffic accidents, and are conducive to ensuring the steady development of the technological level of intelligent vehicle driving assistance. [ABSTRACT FROM AUTHOR]

The research on the factors of heat recovery performance of Enhanced Geothermal Systems (EGS) is an important issue, especially in the well position optimization in EGS, because it can maximize the economic benefits of EGS. Based on the three-dimensional thermo and hydro (TH) single-fracture model, a flow field in the EGS is added to the model, the thermal energy mining of the EGS thermal reservoir is realized through the double well and better study of the impact of regional flow on EGS well placement. To verify the reliability of the three-dimensional numerical model, the comparison between the two-dimensional single fracture model and the single fracture analytical model is performed under the same conditions, and it is found that there is a good agreement between the numerical and the analytical solutions. The influence of the direction of regional flow on the thermal recovery performance of EGS is studied, and the operating lifetime, power generation and heat production rate of the system are used as the evaluation indicators. It is found that there are two stagnation points in the flow field under regional flow conditions, and the stagnation point position changes regularly with regional flow direction. The direction of regional flow has a great influence on the heat extraction ratio and service lifetime of the geothermal system, the layout of the double well must take into account the regional flow. When only considered the influence of regional flow on EGS, after 50 years of EGS operation, the production well temperature and system operating lifetime increase with the increase of β (the angle between the direction of the regional flow and the line connecting the centers of the two wells). When it has regional flow, the greater the well spacing, the greater the temperature of the production well, but when the well spacing increases to a certain value, the well spacing will not affect the temperature of the production well. [ABSTRACT FROM AUTHOR]

This study designs an active equilibrium control strategy based on model predictive control (MPC) for series battery packs. To shorten equalisation time and reduce unnecessary energy consumption, bidirectional active equalisation is modelled and analysed, and the model predictive control algorithm is then applied to the established state space equation. The optimisation problem that minimises the equilibrium time is transformed to a linear programming form in each cycle. By solving the linear programming problem online, a group of control optimal solutions is found and the series equalisation problem is decoupled. The equalisation time is shortened by dynamically adjusting the equalisation current. Simulation results show that the MPC algorithm can avoid unnecessary energy transfer and shorten equalisation time. The bench experimental result shows that the equilibrium time is reduced by 31%, verifying the rationality of the MPC strategy. [ABSTRACT FROM AUTHOR]

To address the problems of low efficiency and high fuel consumption during the cold start and warm-up processes of internal combustion engines, a series hybrid electric vehicle was selected as the research object and two optimal control strategies were designed. A bench test was performed to determine the following: (a) the influence of engine coolant temperature on effective thermal efficiency; and (b) the relationship between engine operating conditions and coolant temperature increase rate. On the basis of the test results, two sets of warm-up process optimization control strategies were designed using a dynamic programming method and a fuzzy control method based on equivalent consumption minimization strategy (ECMS). The test results show that the fuzzy control method for the coolant temperature can effectively shorten the time required to warm up the engine, and the energy consumption of warm-up process can be reduced by nearly 10% through the dynamic programming method. [ABSTRACT FROM AUTHOR]

Crowding occurs when the presence of nearby features causes highly visible objects to become unrecognizable. Although crowding has implications for many everyday tasks and the tremendous amounts of research reflect its importance, surprisingly little is known about how depth affects crowding. Most available studies show that stereoscopic disparity reduces crowding, indicating that crowding may be relatively unimportant in three-dimensional environments. However, most previous studies tested only small stereoscopic differences in depth in which disparity, defocus blur, and accommodation are inconsistent with the real world. Using a novel multi-depth plane display, this study investigated how large (0.54-2.25 diopters), real differences in target-flanker depth, representative of those experienced between many objects in the real world, affect crowding. Our findings show that large differences in target-flanker depth increased crowding in the majority of observers, contrary to previous work showing reduced crowding in the presence of small depth differences. Furthermore, when the target was at fixation depth, crowding was generally more pronounced when the flankers were behind the target as opposed to in front of it. However, when the flankers were at fixation depth, crowding was generally more pronounced when the target was behind the flankers. These findings suggest that crowding from clutter outside the limits of binocular fusion can still have a significant impact on object recognition and visual perception in the peripheral field., Competing Interests: SS, YS, JA, PB No competing interests declared, (© 2023, Smithers et al.)