A comparative study of data-driven battery capacity estimation based on partial charging curves

With its generality and practicality, the combination of partial charging curves and machine learning(ML) for battery capacity estimation has attracted widespread attention. However, a clear classification,fair comparison, and performance rationalization of these methods are lacking, due to the scattered existing studies. To address these issues, we develop 20 capacity estimation methods from three perspectives:charging sequence construction, input forms, and ML models. 22,582 charging curves are generated from 44 cells with different battery chemistry and operating conditions to validate the performance. Through comprehensive and unbiased comparison, the long short-term memory(LSTM) based neural network exhibits the best accuracy and robustness. Across all 6503 tested samples, the mean absolute percentage error(MAPE) for capacity estimation using LSTM is 0.61%, with a maximum error of only 3.94%. Even with the addition of 3 m V voltage noise or the extension of sampling intervals to 60 s, the average MAPE remains below 2%. Furthermore, the charging sequences are provided with physical explanations related to battery degradation to enhance confidence in their application. Recommendations for using other competitive methods are also presented. This work provides valuable insights and guidance for estimating battery capacity based on partial charging curves.

UnderwaterWaste Recognition and Localization Based on Improved YOLOv5

With the continuous development of the economy and society,plastic pollution in rivers,lakes,oceans,and other bodies of water is increasingly severe,posing a serious challenge to underwater ecosystems.Effective cleaning up of underwater litter by robots relies on accurately identifying and locating the plastic waste.However,it often causes significant challenges such as noise interference,low contrast,and blurred textures in underwater optical images.A weighted fusion-based algorithm for enhancing the quality of underwater images is proposed,which combines weighted logarithmic transformations,adaptive gamma correction,improved multi-scale Retinex(MSR)algorithm,and the contrast limited adaptive histogram equalization(CLAHE)algorithm.The proposed algorithm improves brightness,contrast,and color recovery and enhances detail features resulting in better overall image quality.A network framework is proposed in this article based on the YOLOv5 model.MobileViT is used as the backbone of the network framework,detection layer is added to improve the detection capability for small targets,self-attention and mixed-attention modules are introduced to enhance the recognition capability of important features.The cross stage partial(CSP)structure is employed in the spatial pyramid pooling(SPP)section to enrich feature information,and the complete intersection over union(CIOU)loss is replaced with the focal efficient intersection over union(EIOU)loss to accelerate convergence while improving regression accuracy.Experimental results proved that the target recognition algorithm achieved a recognition accuracy of 0.913 and ensured a recognition speed of 45.56 fps/s.Subsequently,Using red,green,blue and depth(RGB-D)camera to construct a system for identifying and locating underwater plastic waste.Experiments were conducted underwater for recognition,localization,and error analysis.The experimental results demonstrate the effectiveness of the proposed method for identifying and locating underwater plastic waste,and it has good localization accuracy.

Non-cooperative target pose estimation based on improved iterative closest point algorithm

For localisation of unknown non-cooperative targets in space,the existence of interference points causes inaccuracy of pose estimation while utilizing point cloud registration.To address this issue,this paper proposes a new iterative closest point(ICP)algorithm combined with distributed weights to intensify the dependability and robustness of the non-cooperative target localisation.As interference points in space have not yet been extensively studied,we classify them into two broad categories,far interference points and near interference points.For the former,the statistical outlier elimination algorithm is employed.For the latter,the Gaussian distributed weights,simultaneously valuing with the variation of the Euclidean distance from each point to the centroid,are commingled to the traditional ICP algorithm.In each iteration,the weight matrix W in connection with the overall localisation is obtained,and the singular value decomposition is adopted to accomplish high-precision estimation of the target pose.Finally,the experiments are implemented by shooting the satellite model and setting the position of interference points.The outcomes suggest that the proposed algorithm can effectively suppress interference points and enhance the accuracy of non-cooperative target pose estimation.When the interference point number reaches about 700,the average error of angle is superior to 0.88°.

Wear behavior of Zn-38Al-3.5Cu-1.2Mg/SiC_(p) composite under different stabilization treatments

A Zn-38Al-3.5Cu-1.2Mg composite reinforced with nano-SiC_(p) was fabricated via stirring-assisted ultrasonic vibration.To improve the abrasive resistance of the Zn-38Al-3.5Cu-1.2Mg/SiC_(p) composite,several stabilization treatments with distinct solid solutions and aging temperatures were designed.The results indicated that the optimal stabilization treatment for the Zn-38Al-3.5Cu-1.2Mg/SiC_(p) composite comprised solution treatment at 380℃for 6 h and aging at 170℃for 48 h.The stabilization treatment led to the formation of dispersive and homogeneous nano-SiC_(p).During the friction wear condition,the nano-SiC_(p) limited the microstructure evolution from the hardα(Al,Zn)phase to the softβ(Al,Zn)phase.Moreover,the increased amount of nano-SiC_(p) improved the grain dimension and contributed to the composite abrasive resistance.Furthermore,the stabilization treatment suppressed the crack initiation and propagation in the friction wear process,thereby improving the abrasive resistance of the Zn-38Al-3.5Cu-1.2Mg/SiC_(p) composite.

Effects of Bionic Volute Tongue on Aerodynamic Performance and Noise Characteristics of Centrifugal Fan Used in the Air-conditioner

The aerodynamic noise generated by the centrifugal fan used in the air conditioner is related to the comfort of human living and working,which can be controlled by using the bionic design and optimization of key components of centrifugal fan.Inspired by the non-smooth leading edge of long-eared owl wing,eight kinds of volute tongues are proposed to reduce the aerodynamic noise of a centrifugal fan.The flow and sound characteristics are numerically investigated by incorporating computational fluid dynamics and computational aero-acoustics.The optimal result exhibits a noise reduction of up to 1.5 dB with a slight increase in mass flow rate.The acoustic characteristics,with respect to the sound pressure level,power spectral density,and sound directivity are discussed.The time-domain,frequency-domain,and root mean square values of pressure fluctuation are monitored and analyzed to assess the unsteady flow interaction between the volute tongue and impeller.The intensity and scale of vortices in the centrifugal fan are suppressed in the upstream and downstream of the bionic volute tongue,and the turbulence effect on the surface of the volute tongue becomes even and weak.

Snake-Hot-Eye-Assisted Multi-Process-Fusion Target Tracking Based on a Roll-Pitch Semi-strapdown Infrared Imaging Seeker

Aiming at intercepting large maneuvering targets precisely,the guidance law of advanced self-seeking missiles requires not only inertial line-of-sight(LOS)angular rate but also target maneuvering acceleration.Moreover,the semi-strapdown stabilization platform has lost the ability to measure the inertial LOS angular rate directly,which needs to be extracted by numerical calculation.The differential operation commonly used in traditional methods can magnify the measurement error of the sensor,resulting in insufficient calculation accuracy of the line-of-sight angular rate.By analyzing the mathematical relationship between the missile-target relative motion and the angle tracking system,a multi-process-fusion integrated filter model of relative motion and angle tracking is presented.To overcome the defect that the infrared seeker cannot directly measure the missile-target distance,following the snake-hot-eye visual mechanism,a visual bionic imaging guidance method of estimating the missile-target relative distance from the infrared images is proposed to improve the observability of the filter model.Finally,target-tracking simulations verify that the estimation accuracy of target acceleration is improved by four times.

Prediction and controlling for welding deformation of propeller base structure

Welding deformation often brings about manufacturing problems such as dimensional inaccuracies during assembly and reduces fabrication efficiency.Prediction and controlling welding deformation can help to improve the quality of welded structures.In this paper,the welding deformation of propeller base struc-tures is predicted by means of numerical approaches and mechanical constraints are proposed to control deformations in welding process.Thermal elasto-plastic finite element method(TEP FEM)is employed to simulate welding process of the base structure.Computed accuracy of welding deformation by TEP FEM analysis is verified by comparing with experimental data of tee joint welding.Results show that welding deformations of the base mainly comprise out-of-plane distortion of ring fringe and radial distortion of cylindrical plate.Exerting mechanical constraints of fixed points at fringe and rigid supports inside cylin-der can decrease out-of-plane and radial distortions effectively.The numerical approach adopted in this article can serve as an effective tool to optimize welding process planning in integrated design method.

Bulk nanocrystalline W-Ti alloys with exceptional mechanical properties and thermal stability

Nanocrystalline(NC)W metals and alloys often exhibit higher radiation tolerance and strength than their coarse-grained counterparts.However,their thermal stability is low,making it difficult to achieve bulk NC W metals and alloys by consolidation using conventional techniques such as pressure-less sintering,hot-explosive-compaction sintering,and spark plasma sintering.Here we report the synthesis and mechanical properties of bulk NC W_(100-x)Ti_(x)(x=10 at.%-30 at.%)alloys prepared by consolidating mechanically alloyed NC powders under a high-temperature/high-pressure condition.Adding 20 at.%-30 at.%Ti largely improves the sinterability of NC W-Ti alloy powders.The room-temperature microhardness and compressive yield strength of consolidated bulk NC W_(80)Ti_(20) alloy are∼16.9 and 6.0 GPa,respectively,which are mainly caused by grain boundary strengthening and significantly higher than those of previously reported W and W alloys.The ultimate compressive strength of bulk NC W_(80)Ti_(20) measured between 900 and 1100°C deceases with increasing temperature.This behavior can be explained by the activation of Rachinger grain boundary sliding.No grain growth is observed in bulk NC W_(80)Ti_(20) after compression at 1000°C.Theoretical calculation suggests that it is the segregation of Ti at grain boundaries that decreases the specific grain boundary free energy and makes the NC W_(80)Ti_(20) alloy thermodynamically stable.