Optimization Control of Multi-Mode Coupling All-Wheel Drive System for Hybrid Vehicle

The all-wheel drive(AWD)hybrid system is a research focus on high-performance new energy vehicles that can meet the demands of dynamic performance and passing ability.Simultaneous optimization of the power and economy of hybrid vehicles becomes an issue.A unique multi-mode coupling(MMC)AWD hybrid system is presented to realize the distributed and centralized driving of the front and rear axles to achieve vectored distribution and full utilization of the system power between the axles of vehicles.Based on the parameters of the benchmarking model of a hybrid vehicle,the best model-predictive control-based energy management strategy is proposed.First,the drive system model was built after the analysis of the MMC-AWD’s drive modes.Next,three fundamental strategies were established to address power distribution adjustment and battery SOC maintenance when the SOC changed,which was followed by the design of a road driving force observer.Then,the energy consumption rate in the average time domain was processed before designing the minimum fuel consumption controller based on the equivalent fuel consumption coefficient.Finally,the advantage of the MMC-AWD was confirmed by comparison with the dynamic performance and economy of the BYD Song PLUS DMI-AWD.The findings indicate that,in comparison to the comparative hybrid system at road adhesion coefficients of 0.8 and 0.6,the MMC-AWD’s capacity to accelerate increases by 5.26%and 7.92%,respectively.When the road adhesion coefficient is 0.8,0.6,and 0.4,the maximum climbing ability increases by 14.22%,12.88%,and 4.55%,respectively.As a result,the dynamic performance is greatly enhanced,and the fuel savings rate per 100 km of mileage reaches 12.06%,which is also very economical.The proposed control strategies for the new hybrid AWD vehicle can optimize the power and economy simultaneously.

Controlled thermally-driven mass transport in carbon nanotubes using carbon hoops

Controlling mass transportation using intrinsic mechanisms is a challenging topic in nanotechnology.Herein,we employ molecular dynamics simulations to investigate the mass transport inside carbon nanotubes(CNT)with temperature gradients,specifically the effects of adding a static carbon hoop to the outside of a CNT on the transport of a nanomotor inside the CNT.We reveal that the underlying mechanism is the uneven potential energy created by the hoops,i.e.,the hoop outside the CNT forms potential energy barriers or wells that affect mass transport inside the CNT.This fundamental control of directional mass transportation may lead to promising routes for nanoscale actuation and energy conversion.

Effects of drive imbalance on the particle emission from a Bose-Einstein condensate in a one-dimensional lattice

Time-periodic driving has been an effective tool in the field of nonequilibrium quantum dynamics,which enables precise control of the particle interactions.We investigate the collective emission of particles from a Bose-Einstein condensate in a one-dimensional lattice with periodic drives that are separate in modulation amplitudes and relative phases.In addition to the enhancement of particle emission,we find that amplitude imbalances lead to energy shift and band broadening,while typical relative phases may give rise to similar gaps.These results offer insights into the specific manipulations of nonequilibrium quantum systems with tone-varying drives.

Optimal Design of the Modular Joint Drive Train for Enhancing Cobot Load Capacity and Dynamic Performance

Automation advancements prompts the extensive integration of collaborative robot(cobot)across a range of industries.Compared to the commonly used design approach of increasing the payload-to-weight ratio of cobot to enhance load capacity,equal attention should be paid to the dynamic response characteristics of cobot during the design process to make the cobot more flexible.In this paper,a new method for designing the drive train parameters of cobot is proposed.Firstly,based on the analysis of factors influencing the load capacity and dynamic response characteristics,design criteria for both aspects are established for cobot with all optimization design criteria normalized within the design domain.Secondly,with the cobot in the horizontal pose,the motor design scheme is discretized and it takes the joint motor diameter and gearbox speed ratio as optimization design variables.Finally,all the discrete values of the optimization objectives are obtained through the enumeration method and the Pareto front is used to select the optimal solution through multi-objective optimization.Base on the cobot design method proposed in this paper,a six-axis cobot is designed and compared with the commercial cobot.The result shows that the load capacity of the designed cobot in this paper reaches 8.4 kg,surpassing the 5 kg load capacity commercial cobot which is used as a benchmark.The minimum resonance frequency of the joints is 42.70 Hz.

A Multiscale Reliability-Based Design Optimization Method for Carbon-Fiber-Reinforced Composite Drive Shafts

Carbon fiber composites,characterized by their high specific strength and low weight,are becoming increasingly crucial in automotive lightweighting.However,current research primarily emphasizes layer count and orientation,often neglecting the potential of microstructural design,constraints in the layup process,and performance reliability.This study,therefore,introduces a multiscale reliability-based design optimization method for carbon fiber-reinforced plastic(CFRP)drive shafts.Initially,parametric modeling of the microscale cell was performed,and its elastic performance parameters were predicted using two homogenization methods,examining the impact of fluctuations in microscale cell parameters on composite material performance.A finite element model of the CFRP drive shaft was then constructed,achieving parameter transfer between microscale and macroscale through Python programming.This enabled an investigation into the influence of both micro and macro design parameters on the CFRP drive shaft’s performance.The Multi-Objective Particle Swarm Optimization(MOPSO)algorithm was enhanced for particle generation and updating strategies,facilitating the resolution of multi-objective reliability optimization problems,including composite material layup process constraints.Case studies demonstrated that this approach leads to over 30%weight reduction in CFRP drive shafts compared to metallic counterparts while satisfying reliability requirements and offering insights for the lightweight design of other vehicle components.

Review of Three-phase Soft Switching Inverters and Challenges for Motor Drives

For electric vehicles (EVs),it is necessary to improve endurance mileage by improving the efficiency.There exists a trend towards increasing the system voltage and switching frequency,contributing to improve charging speed and power density.However,this trend poses significant challenges for high-voltage and high-frequency motor controllers,which are plagued by increased switching losses and pronounced switching oscillations as consequences of hard switching.The deployment of soft switching technology presents a viable solution to mitigate these issues.This paper reviews the applications of soft switching technologies for three-phase inverters and classifies them based on distinct characteristics.For each type of inverter,the advantages and disadvantages are evaluated.Then,the paper introduces the research progress and control methods of soft switching inverters (SSIs).Moreover,it presents a comparative analysis among the conventional hard switching inverters (HSIs),an active clamping resonant DC link inverter (ACRDCLI) and an auxiliary resonant commuted pole inverter (ARCPI).Finally,the problems and prospects of soft switching technology applied to motor controllers for EVs are put forward.

Force Compensation Control for Electro-Hydraulic Servo System with Pump-Valve Compound Drive via QFT-DTOC

Each joint of a hydraulic-driven legged robot adopts a highly integrated hydraulic drive unit(HDU),which features a high power-weight ratio.However,most HDUs are throttling-valve-controlled cylinder systems,which exhibit high energy losses.By contrast,pump control systems offer a high efficiency.Nevertheless,their response ability is unsatisfactory.To fully utilize the advantages of pump and valve control systems,in this study,a new type of pump-valve compound drive system(PCDS)is designed,which can not only effectively reduce the energy loss,but can also ensure the response speed and response accuracy of the HDUs in robot joints to satisfy the performance requirements of robots.Herein,considering the force control requirements of energy conservation,high precision,and fast response of the robot joint HDU,a nonlinear mathematical model of the PCDS force control system is first introduced.In addition,pressure-flow nonlinearity,friction nonlinearity,load complexity and variability,and other factors affecting the system are considered,and a novel force control method based on quantitative feedback theory(QFT)and a disturbance torque observer(DTO)is designed,which is denoted as QFT-DTOC herein.This method improves the control accuracy and robustness of the force control system,reduces the effect of the disturbance torque on the control performance of the servo motor,and improves the overall force control performance of the system.Finally,experimental verification is performed using the PCDS performance test platform.The experimental results and quantitative data show that the QFT-DTOC proposed herein can significantly improve the force control performance of the PCDS.The relevant force control method can be used as a bottom-control method for the hydraulic servo system to provide a foundation for implementing the top-level trajectory planning of the robot.

Effect of CO_(2)flooding in an oil reservoir with strong bottom-water drive in the Tahe Oilfield,Tarim Basin,Northwest China

The dissolution and diffusion of CO_(2)in oil and water and its displacement mechanism were investigated by laboratory experiment and numerical simulation for Block 9 in the Tahe oilfield,a sandstone oil reservoir with strong bottom-water drive in Tarim Basin,Northwest China.Such parameters were analyzed as solubility ratio of CO_(2)in oil,gas and water,interfacial tension,in-situ oil viscosity distribution,remaining oil saturation distribution,and oil compositions.The results show that CO_(2)flooding could control water coning and increase oil production.In the early stage of the injection process,CO_(2)expanded vertically due to gravity differentiation,and extended laterally under the action of strong bottom water in the intermediate and late stages.The CO_(2)got enriched and extended at the oil-water interface,forming a high interfacial tension zone,which inhibited the coning of bottom water to some extent.A miscible region with low interfacial tension formed at the gas injection front,which reduced the in-situ oil viscosity by about 50%.The numerical simulation results show that enhanced oil recovery(EOR)is estimated at 5.72%and the oil exchange ratio of CO_(2)is 0.17 t/t.

基于AVL-DRIVE系统的乘用车双离合变速器换挡品质评价分析

为了解决搭载双离合变速器车辆在低速行驶状态下易出现顿挫现象的问题,提升动力响应能力以及换挡品质,本文以某款双离合变速箱乘用车为研究对象,基于AVL-DRIVE驾驶性评价系统,在不同驾驶模式下对其进行换挡测试。结果表明,不同的驾驶模式下换挡品质存在较大差异,针对评分较低的项目进行分析并提出优化策略,为汽车制造商改进整车品质提供可靠依据。

Heberlein jet technology drives innovations in demanding technical applications

Heberlein Technology AG,Wattwil,Switzerland,March 7,2024–Unique technology for creating advanced technical textiles with exquisite characteristics was featured at the Techtextil in Germany.Heberlein,the leading supplier of air interlacing and air texturing jets,plans to convince visitors to the event with critical components–such as the latest PolyJet-TG-3–that can influence both yarn properties and processes.

毫末智行 自动驾驶大模型 DriveGPT雪湖·海若

行业首个自动驾驶生成式大模型DriveGPT(雪湖·海若)为大模型技术落地自动驾驶提供了新范式。DriveGPT是由毫末自研的自动驾驶认知大模型升级而来,最初目的主要是解决自动驾驶的认知决策优化问题,其基本过程是在认知模块引入真实人驾数据,通过人类反馈强化学习技术,对自动驾驶认知决策模型进行持续优化。当前毫末正在通过引入感知大模型,将DriveGPT升级为通用大模型架构,目标是实现端到端自动驾驶。

Nullmax纽劢 MaxDrive行泊一体方案

是目前市面上极少有的真融合行泊一体4.0形态方案,具有高准度、高稳定、强性能、易落地的产品优势,传感器深度复用、芯片资源共享、安全高效经济,可满足客户各类需求。立足于Nullmax平台化BEV-AI技术架构,兼顾技术性能和部署成本的双重需求,支持高中低不同算力平台,适配任意传感器,可根据需求进行定制化开发,快速完成全场景智能驾驶功能的量产应用,且向上拓展性极强。

Research on Switching Characteristics Based on Optimization Design of SiC MOSFET Drive Circuit

With the increasing emphasis on energy conservation,emission reduction and environmental protection,the application prospect of SiC power devices is becoming more and more broad.In the high frequency application of SiC MOSFET,the change rate of voltage and current in the turn-on and turn-off process increases with the increase of switching frequency.Also,the current and voltage spike oscillation phenomenon is gradually intensified due to the influence of circuit stray parameters.Based on the analysis of SiC MOSFET characteristics,the paper discusses the design requirements and design principles of SiC MOSFET drive circuit.Then,taking the SiC module C2M0080120D of Cree Company as an example,a driver circuit design is realized through the ACPL-355JC optocoupler driver module of Broadcom Company.The circuit not only has the characteristics of fast transmission delay and excellent performance,but also has the functions of overload and short circuit protection.The driving circuit is verified by LTspice simulation software,and the switching characteristics of SiC MOSFET under different working conditions are studied in depth.The experimental results show that the driving circuit can improve the switching time of SiC MOSFET and effectively solve the problem of current and voltage spike oscillation,which lays a foundation for the practical application of SiC MOSFET in the future.

Research on Driver’s Fatigue Detection Based on Information Fusion

Driving fatigue is a physiological phenomenon that often occurs during driving.After the driver enters a fatigued state,the attentionis lax,the response is slow,and the ability todeal with emergencies is significantly reduced,which can easily cause traffic accidents.Therefore,studying driver fatigue detectionmethods is significant in ensuring safe driving.However,the fatigue state of actual drivers is easily interfered with by the external environment(glasses and light),which leads to many problems,such as weak reliability of fatigue driving detection.Moreover,fatigue is a slow process,first manifested in physiological signals and then reflected in human face images.To improve the accuracy and stability of fatigue detection,this paper proposed a driver fatigue detection method based on image information and physiological information,designed a fatigue driving detection device,built a simulation driving experiment platform,and collected facial as well as physiological information of drivers during driving.Finally,the effectiveness of the fatigue detection method was evaluated.Eye movement feature parameters and physiological signal features of drivers’fatigue levels were extracted.The driver fatigue detection model was trained to classify fatigue and non-fatigue states based on the extracted features.Accuracy rates of the image,electroencephalogram(EEG),and blood oxygen signals were 86%,82%,and 71%,separately.Information fusion theory was presented to facilitate the fatigue detection effect;the fatigue features were fused using multiple kernel learning and typical correlation analysis methods to increase the detection accuracy to 94%.It can be seen that the fatigue driving detectionmethod based onmulti-source feature fusion effectively detected driver fatigue state,and the accuracy rate was higher than that of a single information source.In summary,fatigue drivingmonitoring has broad development prospects and can be used in traffic accident prevention and wearable driver fatigue recognition.

Uphill or downhill?Cropland use change and its drivers from the perspective of slope spectrum

The continuous decrease of low-slope cropland resources caused by construction land crowding poses huge threat to regional sustainable development and food security.Slope spectrum analysis of topographic and geomorphic features is considered as a digital terrain analysis method which reflects the macro-topographic features by using micro-topographic factors.However,pieces of studies have extended the concept of slope spectrum in the field of geoscience to construction land to explore its expansion law,while research on the slope trend of cropland from that perspective remains rare.To address the gap,in virtue of spatial analysis and geographically weighted regression(GWR)model,the cropland use change in the Yangtze River Basin(YRB)from 2000 to 2020 was analyzed and the driving factors were explored from the perspective of slope spectrum.Results showed that the slope spectrum curves of cropland area-frequency in the YRB showed a first upward then a downward trend.The change curve of the slope spectrum of cropland in each province(municipality)exhibited various distribution patterns.Quantitative analysis of morphological parameters of cropland slope spectrum revealed that the further down the YRB,the stronger the flattening characteristics,the more obvious the concentration.The province experienced the greatest downhill cropland climbing(CLC)was Shannxi,while province experienced the highest uphill CLC was Zhejiang.The most common cropland use change type in the YRB was horizontal expansion type.The factors affecting average cropland climbing index(ACCI)were quite stable in different periods,while population density(POP)changed from negative to positive during the study period.This research is of practical significance for the rational utilization of cropland at the watershed scale.

Systematic improvement in Tong's B-type water drive method

Tong's B-type water drive method was proposed as early as the 1970s and has been widely applied in the dynamic prediction and effective evaluation of oilfield development.Through extensive applications and studies,many researchers found that the statistical constants in the formula of the Tong's B-type water drive method(also referred to as the Tong's B-type formula)are not applicable to multiple types of reservoirs,especially low-permeability ones,due to the limited range of reservoir types when the formula was conceived.Moreover,they put forward suggestions to improve the Tong's B-type formula,most of which focused on the research and calculation of the first constant in the formula.For oilfields in the development stages of high or ultra-high water cuts,it is widely accepted that different types of reservoirs have different limit water cuts.This understanding naturally makes it necessary to further modify the Tong's B-type formula.It is practically significant to establish the water drive formula and cross plot considering that the two constants in the formula vary with reservoir type.By analyzing the derivation process and conditions of the Tong's B-type formula,this study points out two key problems,i.e.,the two constants 7.5 and 1.69 in the formula are not applicable to all types of reservoir.Given this,this study establishes a function between key reservoir parameters and the first constant and another function between key reservoir parameters and recovery efficiency.Based on the established two functions and considering that different types of oil reservoir have different limit water cuts,this study develops an improved Tong's B-type formula and prepares the corresponding improved cross plot.The results of this study will improve the applicability and accuracy of Tong's B-type water drive method in predicting the trend of water cut increasing for different types of oil reservoirs.

T_(2)W-DRIVE-HR序列在钆造影核磁共振成像检测豚鼠实验性内耳内淋巴积水中的应用

目的研究驱动平衡自旋回波序列(T_(2)W-DRIVE-HR)在钆造影核磁共振成像(MRI)时检测豚鼠内耳内淋巴积水的效果。方法8只雌性豚鼠分为两组,包括对照组和内淋巴积水模型组(腹腔注射醋酸去氨加压素),各组相应处理17天后进行内耳磁共振成像和组织形态学检查。结果T_(2)W-DRIVE-HR序列在矢状位扫描时内耳成像效果好。内耳磁共振成像结果表明,与对照组相比,积水模型组豚鼠耳蜗内淋巴体积明显增加(t=3.594,P<0.05);组织形态学结果表明,与对照组相比,积水模型组豚鼠耳蜗每一转中阶面积/(前庭阶面积+中阶面积)的比值明显升高(t=3.105,P<0.05)。结论腹腔注射醋酸去氨加压素诱导豚鼠内耳内淋巴积水,使用T_(2)W-DRIVE-HR序列在钆造影磁共振成像检测的内耳内淋巴积水与组织形态学检测结果一致,说明该序列可用于豚鼠内耳内淋巴积水的检测。

Fault Detection for Motor Drive Control System of Industrial Robots Using CNN-LSTM-based Observers

The complex working conditions and nonlinear characteristics of the motor drive control system of industrial robots make it difficult to detect faults.In this paper,a deep learning-based observer,which combines the convolutional neural network(CNN)and the long short-term memory network(LSTM),is employed to approximate the nonlinear driving control system.CNN layers are introduced to extract dynamic features of the data,whereas LSTM layers perform time-sequential prediction of the target system.In terms of application,normal samples are fed into the observer to build an offline prediction model for the target system.The trained CNN-LSTM-based observer is then deployed along with the target system to estimate the system outputs.Online fault detection can be realized by analyzing the residuals.Finally,an application of the proposed fault detection method to a brushless DC motor drive system is given to verify the effectiveness of the proposed scheme.Simulation results indicate the impressive fault detection capability of the presented method for driving control systems of industrial robots.

Research on Energy Efficiency Characteristics of Mining Shovel Hoisting and Slewing System Driven by Hydraulic-Electric Hybrid System

Mining shovel is a crucial piece of equipment for high-efficiency production in open-pit mining and stands as one of the largest energy consumption sources in mining.However,substantial energy waste occurs during the descent of the hoisting system or the deceleration of the slewing platform.To reduce the energy loss,an innovative hydrau-lic-electric hybrid drive system is proposed,in which a hydraulic pump/motor connected with an accumulator is added to assist the electric motor to drive the hoisting system or slewing platform,recycling kinetic and potential energy.The utilization of the kinetic and potential energy reduces the energy loss and installed power of the min-ing shovel.Meanwhile,the reliability of the mining shovel pure electric drive system also can be increased.In this paper,the hydraulic-electric hybrid driving principle is introduced,a small-scale testbed is set up to verify the feasibil-ity of the system,and a co-simulation model of the proposed system is established to clarify the system operation and energy characteristics.The test and simulation results show that,by adopting the proposed system,compared with the traditional purely electric driving system,the peak power and energy consumption of the hoisting electric motor are reduced by 36.7%and 29.7%,respectively.Similarly,the slewing electric motor experiences a significant decrease in peak power by 86.9%and a reduction in energy consumption by 59.4%.The proposed system expands the application area of the hydraulic electric hybrid drive system and provides a reference for its application in over-sized and super heavy equipment.

Three-Dimensional Conjugate Tooth Surface Design and Contact Analysis of Harmonic Drive with Double-Circular-Arc Tooth Profle

A three-dimensional conjugate tooth surface design method for Harmonic Drive with a double-circular-arc tooth profle is proposed. The radial deformation function of the fexspline (FS), obtained through Finite Element (FE) analysis, is incorporated into the kinematics model. By analyzing the FS tooth enveloping process, the optimization of the overlapping conjugate tooth profle is achieved. By utilizing the hobbing process, the three-dimensional machinable tooth surface of FS can be acquired. Utilizing the coning deformation of the FS, simulations are conducted to analyze the multi-section assembly and meshing motion of the machinable tooth surface. The FE method is utilized to analyze and compare the loaded contact characteristics. Results demonstrate that the proposed design method can achieve an internal gear pair consisting of a circular spline with a spur gear tooth surface and the FS with a machinable tooth surface. With the rated torque, approximately 24% of the FS teeth are engaged in meshing, and more than 4/5 of the tooth surface in the axial direction carries the load. The contact patterns, maximum contact pressure, and transmission error of the machinable tooth surface are 227.2%, 40.67%, and 71.24% of those on the spur gear tooth surface, respectively. It clearly demonstrates exceptional transmission performance.