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The loader is one of the most widely used pieces of engineering machinery in the world for soil transportation, loading and unloading materials, and low-intensity shovel digging operations in harsh and complex operating conditions; it requires very frequent shifting and has other challenging characteristics. In order to realize automatic frequent shifting, we need to better design the shifting rules in the shifting process, improve the shifting quality and working efficiency, and solve the key engineering problems of energy saving and high efficiency in the shifting process of loaders. In this paper, a 7-ton wheel loader is taken as the research object, the loader shoveling process of the four operating modes is analyzed, and a multi-mode variable parameter shift law is designed. Aiming at the complicated and nonlinear characteristics of the power transmission system of the loader, an intelligent shift control method based on an RBF neural network is proposed. Finally, the simulation test and the clutch shift oil pressure test are carried out. From the test results, the clutch test oil pressure curve obviously shows a four-stage upward trend during shifting, and the buffering effect is obvious. The designed multi-mode variable-parameter intelligent shift law of the loader is reasonable and feasible, and the shift recognition rate reaches 97.92%, which provides theoretical support for the realization of intelligent automatic speed change control of the loader, and it certainly has engineering value. [ABSTRACT FROM AUTHOR]

The extensive deployment of camera-based IoT devices in our society is heightening the vulnerability of citizens’ sensitive information and individual data privacy. In this context, thermal imaging techniques become essential for data desensitization, entailing the elimination of sensitive data to safeguard individual privacy. Meanwhile, thermal imaging techniques can also play a important role in industry by considering the industrial environment with low resolution, high noise and unclear objects’ features. Moreover, existing works often process the entire video as a single entity, which results in suboptimal robustness by overlooking individual actions occurring at different times. In this paper, we propose a lightweight algorithm for action recognition in thermal infrared videos using human skeletons to address this. Our approach includes YOLOv7-tiny for target detection, Alphapose for pose estimation, dynamic skeleton modeling, and Graph Convolutional Networks (GCN) for spatial-temporal feature extraction in action prediction. To overcome detection and pose challenges, we created OQ35-human and OQ35-keypoint datasets for training. Besides, the proposed model enhances robustness by using visible spectrum data for GCN training. Furthermore, we introduce the two-stream shift Graph Convolutional Network to improve the action recognition accuracy. Our experimental results on the custom thermal infrared action dataset (InfAR-skeleton) demonstrate Top-1 accuracy of 88.06% and Top-5 accuracy of 98.28%. On the filtered kinetics-skeleton dataset, the algorithm achieves Top-1 accuracy of 55.26% and Top-5 accuracy of 83.98%. Thermal Infrared Action Recognition ensures the protection of individual privacy while meeting the requirements of action recognition. [ABSTRACT FROM AUTHOR]

In order to improve the performance of sliding mode observer in detecting the state of induction motor, a state detection method based on particle swarm optimization (PSO)-backstepping (BS)-sliding mode observer (SMO) is proposed in this paper. In this method, the controller is constructed and the parameters of the control rate are optimized, so that the tracking accuracy and robustness of the new observer are improved relative to conventional observer, exponential observer, PI and PID. Firstly, the state equation of the induction motor under stator and rotor winding fault and stator current sensor fault is established. Secondly, the new sliding mode observer is designed using the backstepping method based on the new reaching law. Then, the new fitness function and PSO is used to optimize the parameters of the new sliding mode observer. Finally, the simulation comparison experiment of stator current state detection is carried out under the simulated fault condition of induction motor. The feasibility of the method is verified by comparing the state tracking situation and the state detection error. The comparative experimental results show that the method has less jitter, stronger robustness, and higher state tracking accuracy when detecting stator current states under different faults. [ABSTRACT FROM AUTHOR]

In order to study the group cooperative control method of multiple intelligent networked vehicles, the multiple intelligent networked vehicles can move in the form of a fleet. Based on the leader–follower method, the paper optimizes the control effect of the leader–follower method by solving the error transmission phenomenon in the leader–follower method. In this paper, the modeling of the multiple intelligent connected vehicle adopts the vehicle dynamics model and the Magic Formula/Swift Magic tire model, and adopts the model predictive control (MPC) dynamics trajectory tracking controller for control. Through the CarSim–Simulink multi-vehicle dynamics co-simulation platform established in this paper, the group cooperative control experiments of multiple intelligent networked vehicles under different working conditions were carried out for simulation verification. The analysis results show that the maximum average error of the proposed method decreases from 8.802 to 0.094 in the case of straight line and 0.669 to 0.379 in the case of curve tracking, which proves that the method can effectively reduce the transmission of errors. [ABSTRACT FROM AUTHOR]

Summary: Conventional fault recognition algorithms can only recognize the classes of faults that have appeared in wind turbine systems. However, if a new category of faults appears, the traditional algorithm can only misclassify it into the class of pre‐existing faults. In this paper, a new class fault recognition method based on deep learning is proposed. Firstly, the initialized model is built using known fault data, including detectors and classifiers. Secondly, the new class fault data detected by the detectors are put into the cache, and when the cache overflows, the new class faults are augmented with data using generative adversarial networks. Finally, the new class fault data and the generated data are added to the initial training set, and the structure and weights of the initialized model are updated using the new training set. In this way, the purpose of recognizing the new class of faults is achieved. The experimental results show that the proposed detection algorithm can effectively detect new class faults and the model can efficiently solve the problem of the new class of fault recognition after data augmentation. [ABSTRACT FROM AUTHOR]

The rapid advancement of deep learning has significantly heightened the threats posed by Side-Channel Attacks (SCAs) to information security, transforming their effectiveness to a degree several orders of magnitude superior to conventional signal processing techniques. However, the majority of existing Deep-Learning Side-Channel Attacks (DLSCAs) primarily focus on the classification accuracy of the trained model at the attack stage, often assuming that adversaries have unlimited computational and time resources during the profiling stage. This might result in an inflated assessment of the trained model's fitting capability in a real attack scenario. In this paper, we present a novel DLSCA model, called a Multi-Dimensional Fusion Convolutional Residual Dendrite (MD_CResDD) network, to enhance and speed up the feature extraction process by incorporating a multi-scale feature fusion mechanism. By testing the proposed model on two software implementations of AES-128, we show that it is feasible to improve the profiling speed by at least 34% compared to other existing deep-learning models for DLSCAs and meanwhile achieved a certain level of improvement (8.4% and 0.8% for two implementations) in the attack accuracy. Furthermore, we also investigate how different fusion approaches, fusion times, and residual blocks can affect the attack efficiency on the same two datasets. [ABSTRACT FROM AUTHOR]

Purpose: With the development of integrated circuit and communication technology, digital secure communication has become a research hotspot. This paper aims to design a five-dimensional fractional-order chaotic secure communication circuit with sliding mode synchronous based on microcontroller (MCU). Design/methodology/approach: First, a five-dimensional fractional-order chaotic system for encryption is constructed. The approximate numerical solution of fractional-order chaotic system is calculated by Adomian decomposition method, and the phase diagram is obtained. Then, combined with the complexity and 0–1 test algorithm, the parameters of fractional-order chaotic system for encryption are selected. In addition, a sliding mode controller based on the new reaching law is constructed, and its stability is proved. The chaotic system can be synchronized in a short time by using sliding mode control synchronization. Findings: The electronic circuit is implemented to verify the feasibility and effectiveness of the designed scheme. Originality/value: It is feasible to realize fractional-order chaotic secure communication using MCU, and further reducing the synchronization error is the focus of future work. [ABSTRACT FROM AUTHOR]

The traditional traction control system (TCS) based on hydraulic braking only works when the wheels are slipping, which will cause the problem of slow response to extreme slip. In addition, the TCS of four‐wheel‐independent‐drive electric vehicle (4WIDEV) is often based on road adhesion characteristics identification or optimal slip ratio identification to implement active control, which is difficult to achieve in engineering. Aiming at this problem, a practical active TCS is proposed in this paper. Firstly, according to the wheel slip state of the front and rear axles, the dynamic transfer of torque between axles is realized to maintain the vehicle propulsion power. Second, the adhesion conditions between road and tire are classified, and two sets of target slip ratio thresholds are formulated for high and low adhesion pavement, respectively. Then the current road adhesion coefficient is estimated by using the advantage that the in‐wheel motor torque can be obtained in real‐time. Thirdly, the overall framework of the control strategy is established, the logic threshold control algorithm is adopted for tracking the wheel target slip ratio. Finally, the simulation results show that the proposed active TCS can improve the vehicle power and avoid excessive wheel slipping. [ABSTRACT FROM AUTHOR]

For the sake of deriving the signature frequencies of the composite malfunctions of broken and worn teeth of gearbox, the essay raised a method on account of local mean decomposition and Hilbert demodulation to diagnose gearbox composite fault. The local mean decomposition translates a complicated multi-component AM-FM signal into several PF elements with certain physical significance. Every PF component can be approximately regarded as a simplex component AM-FM signal. Using correlation coefficient method, the Hilbert envelope demodulation spectrum is analyzed by selecting the PF component which is strongly relevant to the incipient composite malfunction signal, and the malfunction signature frequency is identified from the demodulation spectrum. Through analyzing the emulation signal and the vibration signal of the actual gear box broken-wear complex fault, and compared with EMD, it is shown that the means can effectually discern the malfunction characteristic frequency in the composite fault signal. [ABSTRACT FROM AUTHOR]

The driving range of electric vehicles (EVs) is still an important factor restricting their development. Although the rising battery energy density has reached a bottleneck, which is a key constraint, the drive energy management strategy also has a significant effect and can improve the driving range of EVs, since wheel traction torque control can directly optimize the driving energy consumption of EVs. In order to comprehensively analyze the current research status of driving energy management and clarify its development direction, this review focuses on the driving energy management strategy of EVs and systematically summarizes the configurations and power distribution strategies of the dual-motor coupling drive system (DCDS), as well as torque vectoring control strategies of the decentralized drive system. Firstly, driving energy losses are analyzed in detail, which mainly include electric loss, tire slip energy dissipation, and the power of cornering resistance. Secondly, typical configurations of the DCDS are introduced, and the power distribution strategies of the DCDS are comprehensively reviewed. Finally, as an interesting energy-saving technology, energy-saving torque vectoring, generally applied to decentralized drive systems, is reviewed in detail in terms of its energy-saving pathways and control strategies, which are classified as front-and-rear torque vectoring and left-and-right torque vectoring. Research findings indicate that the driving range of EVs can be effectively increased by applying a driving energy management strategy based on several novel multi-power source drive systems. The development of a driving energy management strategy and the required novel drive systems will be a valuable and crucial direction for further energy conservation in EVs. [ABSTRACT FROM AUTHOR]

In order to accurately track the state of the doubly fed induction generator (DFIG) for wind power systems and implement its fault detection, an event-triggered sliding mode observer based on particle swarm optimization (PSO-ET-SMO) is proposed in this paper. First, the sliding mode observer (SMO) is designed according to the rotor current state space equation of the DFIG. Then, a new event-triggered mechanism (NETM) is proposed for the designed SMO, and the constructed fitness function is optimized by using Particle Swarm Optimization (PSO) algorithm, so as to obtain the optimal parameters of the SMO. Finally, to verify the effectiveness of the proposed method in this paper, the fault detection of the DFIG for wind power systems is performed by using the sequence of residuals between the rotor current output values and the sliding mode observations. The innovation of this work is that a NETM is designed by combining sliding mode reaching law, and this mechanism is introduced into the design of SMO. The simulation results show that the proposed method not only reduces sliding mode chattering, but also improves the tracking effect of SMO and detects faults accurately. At the same time, due to the action of the NETM, the transmission burden between the SMO and the DFIG is also reduced. [ABSTRACT FROM AUTHOR]

Various physical information can be leaked while the encryption algorithm is running in the device. Side-channel analysis exploits these leakages to recover keys. Due to the sensitivity of deep learning to the data features, the efficiency and accuracy of side channel analysis are effectively improved with the application of deep learning algorithms. However, a considerable part of existing reserches are based on traditional neural networks. The effectiveness of key recovery is improved by increasing the size of the network. However, the computational complexity of the algorithm increases accordingly. Problems such as overfitting, low training efficiency, and low feature extraction ability also occur. In this paper, we construct an improved lightweight convolutional neural network based on the feature fusion network. The new network and the traditional neural networks are respectively applied to the side-channel analysis for comparative experiments. The results show that the new network has faster convergence, better robustness and higher accuracy. No overfitting has occurred. A heatmap visualization method was introduced for analysis. The new network has higher heat value and more concentration in the key interval. Side-channel analysis based on feature fusion network has better performance, compared with the ones based on traditional neural networks. [ABSTRACT FROM AUTHOR]

Traditional fault recognition algorithms can only identify the known classes of fault in wind turbine systems(WTs). These faults have already appearedin the WTs, thus, the fault recognition algorithm can identify the classesof them. However, if a new classfault didnot happen in the WTs before, the traditional fault recognition algorithm can only identify it as known class of fault, which results in an incorrect identification. Toaddress this problem, a new class fault recognition method based on semi-supervised deep learning(SDL-NCFR)is proposed. Firstly, multiple WTs signals are used as input and features are extracted by convolutional autoencoder network; secondly, the initialization model is built with compressed features as input to the classifier and error feature map as input to the detector; finally, the detector will put new class fault instances into the buffer. When the buffer overflows, the algorithm starts to update, thus achieving the purpose of identifying new class faults. The experimental results show that the average accuracy of the initialized model could reach more than 98%. The accuracy of updated model could still reach 89.39%, and the detection rate could reach 99.50%, the recall reached 88.76%, the precision reached 92.03%, and the F1 score reached 90.36% respectively. The experimental results show that the proposed algorithm can effectively solve the problem of identifying new class faults in WTs, and the accuracy is much higher than that of traditional detection methods. [ABSTRACT FROM AUTHOR]

To improve the ride comfort of wheeled armored vehicles, air springs are used. To describe the vehicle motion more accurately, a nine-degree-of-freedom air suspension system for the whole vehicle was established, and its equations of motion were derived. Through theoretical analysis of the stiffness characteristics and forces on the air springs, the nonlinear restoring force was obtained as a cubic polynomial of the air spring displacement. The simulation results obtained by fitting the polynomial and radial basis function curves with MATLAB/Simulink software are consistent with the actual test results, thus verifying the correctness of the nonlinear air spring polynomial model. Finally, a fuzzy fractional order PIλDμ controller is designed and simulated for the vehicle-seat-body model in terms of wheel dynamic load, suspension dynamic deflection, body acceleration, and other indicators. The simulation results show that the fuzzy fractional order PIλDμ Proportion Integral Differential (PID) control strategy has better overall performance than the PID control strategy, fuzzy control strategy, and fuzzy PID control strategy. [ABSTRACT FROM AUTHOR]

In this article, a real-time vehicle sideslip angle state observer is proposed, which is based on the EKF algorithm. Firstly, based on a 2-DOF dynamical model and the tire lateral force model, the vehicle sideslip angle state observer model with a self-adapted truncation procedure is established by combining the EKF and the least squares methods. The calculation of the Jacobi matrix in the time domain is transformed into a calculation in the frequency domain. A self-adapted update noise estimation method and an initial value setting strategy are proposed as well. Finally, a hardware-in-the-loop simulation is carried out by Matlab/Simulink-CarSim-dSPACE, and the real-time reliability of the estimation method is verified and analyzed by RMSE. [ABSTRACT FROM AUTHOR]

The majority of recently demonstrated Deep-Learning Side-Channel Analysis (DLSCA) use neural networks trained on a segment of traces containing operations only related to the target subkey. However, when the size of the training set is limited, as in this paper with only 5K power traces, the deep learning (DL) model cannot effectively learn the internal features of the data due to insufficient training data. In this paper, we propose a cross-subkey training approach that acts as a trace augmentation. We train deep-learning models not only on a segment of traces containing the SBox operation of the target subkey of AES-128 but also on segments for other 15 subkeys. Experimental results show that the accuracy of the subkey combination training model is 28.20 % higher than that of the individual subkey training model on traces captured in the microcontroller implementation of the STM32F3 with AES-128. And validation is performed on two additional publicly available datasets. At the same time, the number of traces that need to be captured when the model is trained is greatly reduced, demonstrating the effectiveness and practicality of the method. [ABSTRACT FROM AUTHOR]

This paper aims to study the torque optimization control of distributed drive electric vehicles in the cornering process and reduce the cornering energy consumption. The main energy consumption of the vehicle in the cornering process is analyzed clearly based on the 7-DOF vehicle dynamics model. The torque vectoring distribution (TVD) of a distributed drive electric vehicle in the process of turning was studied on the basis of the Pontryagin Minimum Principle (PMP). The Beetle Antenna Search–Particle Swarm Optimization (BAS-PSO) algorithm was used to optimize the torque distribution coefficient offline, and the algorithm was improved to improve the operation speed. Based on the vehicle dynamics characteristics, the table of torque distribution coefficient of minimum turning energy consumption and the optimal energy-saving degree of TVD control in different bending conditions were worked out. [ABSTRACT FROM AUTHOR]

In this paper, a five-dimensional hyperchaotic system for secure communication based on MCU is proposed. First, a five-dimensional hyperchaotic system is constructed and the features such as equilibrium stability and Lyapunov exponents are analyzed. The states of this system with different parameters are studied, including periodic, hyperchaotic and chaotic state. Next, the complexity of system is analyzed. By analyzing the complexity of each dimension, the correctness of system selection is verified and the dimension with the highest complexity is selected for communication encryption. Then, the hardware circuit is designed. The encryption and decryption of square wave signal are completed through simulation which realizing the secure communication. Finally, the accuracy of system and the algorithm of realizing chaos of MCU are adjusted, and the system after adjustment is in a chaotic state verified by 0–1 test, the chaos is successfully generated by MCU. Using MCU can reduce the hardware cost of signal encryption greatly, and apply five-dimensional hyperchaotic system to secure communication can not only improve the security but also enhance the anti-interference ability in the process of signal transmission. [ABSTRACT FROM AUTHOR]

Purpose: In this paper, a nine-dimensional chaotic system is designed and applied to secure communication. Design/methodology/approach: Firstly, the equilibrium characteristics, dissipativity, bifurcation diagram and Lyapunov exponent spectrum are used to analyze the relevant characteristics of the proposed nine-dimensional chaotic system. In the analysis of Lyapunov exponential spectrum, when changing the linear parameters, the system shows two states, hyperchaos and chaos. For secure communication, there is a large secret key space. Secondly, C0 complexity and SEcomplexity of the system are analyzed, which shows that the system has sequences closer to random sequences. Findings: The proposed nine-dimensional system has a large key space and more complex dynamic characteristics Originality/value: The results show that the proposed nine-dimensional hyperchaotic system has excellent encryption capabilities and can play an important role in the field of secure communication. [ABSTRACT FROM AUTHOR]

Buck converter is a kind of converter device with high efficiency, wide adjustable output voltage, small loss, small size and light weight. Its circuit has nonlinearity and will exhibit abundant nonlinear phenomena with the change of circuit parameters, resulting in unstable output voltage and being susceptible to load and external disturbances. Therefore, a control method based on Extreme Learning Machine (ELM) combined with a proportionalintegral-derivative (PID) controller is proposed in this paper and used for output voltage control of buck converter. Firstly, the mathematical model of buck converter in Continuous Conduction Mode (CCM) is established by the state space averaging method. Then the PID tuning algorithm is designed in combination with ELM, and the stability analysis of the model is carried out. Finally, the simulation experiment is carried out under different disturbances. By comparing with the open-loop control strategy, the effectiveness of the proposed ELM-PID control strategy is verified, indicating that the proposed method can achieve the stability of output voltage and good dynamic response. [ABSTRACT FROM AUTHOR]

To detect the fault of the doubly-fed induction generator (DFIG), in this paper, a fault detection method of novel sliding mode observer is proposed, without a velocity sensor. In addition to better elimination of chattering for the new sliding mode observer, at the same time, it has better stability and faster convergence speed than the traditional sliding mode observer. Firstly, the sliding mode observer is built according to the mathematical model of the DFIG. Then, the rotor current and the rotational speed are estimated. After comparing the actual rotor current value with the observed value, the self-detection of the fault for the DFIG is realized. Secondly, three faults of grid voltage sags failure, DFIG inter-turn stator fault and rotor current sensor fault are given respectively. After that the Simulink simulation model is built under different fault conditions. It is proved by simulation that this sliding mode observer can well detect faults occurring at different positions. Additionally, it can also be proved that the sliding mode observer has the characteristics of fast response and good stability. [ABSTRACT FROM AUTHOR]

This paper proposes a novel seven-dimensional fractional-order chaotic system applied to secure communication. First, a seven-dimensional fractional order system is constructed by selecting an arbitrary fractional order q. Then, the chaotic and non-chaotic dynamic behaviors of the system are analyzed by 0–1 Test, and the correctness is verified by phase diagram comparison and complexity analysis. Then, the corresponding fractional-order chaotic circuit is designed, and the phase image which is basically consistent with the experimental simulation results is obtained. Finally, the seven-dimensional fractional-order chaotic system is used to complete the signal encryption and decryption test to achieve secure communication. This indicates that the seven-dimensional fractional-order chaotic system proposed to this paper has more complex dynamic characteristics and can be used for secure communication. [ABSTRACT FROM AUTHOR]

Photocatalytic water splitting for hydrogen production has attracted increasing research attention in recent years, and great efforts have been made in order to find the ideal photocatalyst. In this work, we proposed a two-dimensional material-based van der Waals (vdW) heterostructure constructed by vertically stacked indium selenide (InSe) and silicane (SiH) and studied the feasibility of using it as a possible photocatalyst for water splitting by using first-principles methods. The results show that the InSe/SiH is a direct band gap semiconductor with appropriate gap value and band edge position for photocatalysts in water splitting. Importantly, this heterostructure presents type-II band alignment at the equilibrium configuration, which supports the effective separation of photoexcited electrons and holes. A built-in electric field set up within the interface of the heterostructure will further hinder the electron–hole recombination and thus improve the photocatalytic efficiency. In addition, compared with separated InSe and SiH monolayers, the heterostructure exhibits enhanced light absorption capabilities in ultraviolet and visible light regions. These findings indicate that the InSe/SiH vdW heterostructure is a promising candidate for photocatalysts for solar water splitting. [ABSTRACT FROM AUTHOR]

Summary: In the paper, a novel six‐dimensional hyperchaotic system is proposed for secure communication. A detailed analysis of some relevant features, such as equilibrium stability, dissipation, bifurcation, and Lyapunov exponents. The results show that there are hyperchaos, chaos, quasi‐periodic, and periodic dynamic behaviors for our constructed hyperchaotic system. Then the six‐dimensional hyperchaotic system is simulated and implemented, which verified its physical realizability. The six‐dimensional hyperchaotic system is applied to complete the signal encryption and decryption circuit design, then the circuit is tested. The results indicate that the proposed hyperchaotic system can play an important role in some key areas such as communication security and complex systems. [ABSTRACT FROM AUTHOR]

The differential drive assist steering (DDAS) system improves the handiness of steering by torque difference between two-side wheels. However, the DDAS and the vehicle stability control (VSC) will interfere with each other due to the same actuators. This paper proposes a hierarchical coordination control approach for the DDAS and VSC. The system is divided into four layers: the parameter estimation layer, the control region division layer, the coordination decision layer, and the control allocation layer. In the control region division layer, the stable area is firstly determined on the $\beta - \dot{\beta }$ phase portrait, and the coordinated control region is obtained by applying the simulated annealing algorithm inwardly and outwardly to the boundary of the stable area. In the coordination decision layer, the direct control strategy of steering hand wheel torque is used to establish the controller of the DDAS based on the fuzzy adaptive PID algorithm. At the same time, the controller of yaw moment is established based on the sliding mode control theory. In the stable region, the DDAS works separately. The VSC works separately in the unstable region. In the coordination region, the Sigmoid function is used to adjust the weight coefficient of the DDAS dynamically. Besides, the control allocation layer includes torque distribution controller and vehicle slip ratio controller. Finally, the vehicle model is established in CarSim, the control strategy, and the wheel motor model are established in Simulink. The joint simulation results of several typical conditions show that the designed control strategy can improve the handling stability of the vehicle effectively while DDAS working even in severe driving condition. [ABSTRACT FROM AUTHOR]

Electrical Parking Brake (EPB) has been popularly used in passenger cars over the past ten years. With the help of the several kinds of sensors mounted in driveline for the total traction force estimation, EPB can obtain well performance on drive-off assistance and automatic parking brake. Furthermore, its AUTOHOLD function can realize automatic parking brake and ease the driver. However, given that the higher cost and complexity of this traction force estimation method based on the driveline sensors and its slower response resulted by applying maximum parking force for safety parking while driving off, a novel automatic parking brake system without these transmission system sensors, such as clutch position sensor for the cars with manual transmission, is proposed in this paper, including its control scheme and application test. Firstly, the indirect judgement method of the appropriate moment to release the parking brake, which is based on the car pitch moment when it drives off, is introduced according to the force analysis when the car is ready to go. Then a pragmatic mass estimation method for proper brake force calculation is proposed for improving the drive-off performance. In addition, for the convenience and drivability of skillful driver, as well as the system reliability, a mechanical redundant design to reserve the conventional handbrake lever is also described. Finally, various simulations based on CarSim software and road tests are performed to validate its effectiveness. [ABSTRACT FROM AUTHOR]

Although electric vehicle fully exhibits its comparative merits of energy conservation and environmental friendliness, further improvement of its traction energy efficiency lacks comprehensive investigations in the past. In this paper, the effect of the torque vectoring on traction energy conservation during cornering for a rear-wheel-independent-drive electric vehicle is investigated. Firstly, turning resistance coefficient and energy conservation mechanism of torque vectoring are derived from the single track dynamic model. Next, an optimal torque vectoring control strategy based on genetic algorithm is proposed, with the consideration of the influence of the operation-point change of the in-wheel motors, to find out the best torque vectoring ratio offline. Finally, various simulation tests are conducted to validate the energy conservation effect after Simulink modelling. The results verify that though the optimization of the operating region of the motors is the main part for tractive energy conservation, the contribution of torque vectoring itself can reach up to 1.7% in some typical cases. [ABSTRACT FROM AUTHOR]

Pursuing more efficient running and operation has been the orientation of today's automotive powertrain and chassis technologies development. As for the brake system, increasing the braking efficiency or reducing the power consumption of actuation is an effective method to improve the energy-saving performance of the car. In this paper, a self-energizing wedge disc brake actuated by hydraulic power, that could be used to reduce both the actuation force and the energy required for braking is presented and compared in terms of the practical issues on braking efficiency, heat recession and self-locking. The selected critical parameters and their optimum value range are determined based on the static force analysis. Then the simulations based on AMESim/Simulink are implemented to compare the proposed wedge brake with traditional disc brake, and to investigate the influence of the wedge angle and the actuating angle on the braking efficiency performance. The simulation results validate the self-reinforcement and energy saving performance of this hydraulic wedge disc brake well. [ABSTRACT FROM AUTHOR]

Developing fault detection is very important for improving the equipment reliability and saving energy consumption. As compared with neural networks, extreme learning machine (ELM) is based on statistical learning theory, which has advantages of better classification ability and generalization performance. This paper presents a novel approach for fault detection and diagnosis based on Firefly-Chaos Algorithm and Extreme Learning Machine. The experiment result indicates this proposed method is effective for Analog Circuit fault detection and diagnosis and the model generalization ability is favorable. [ABSTRACT FROM AUTHOR]

The anxiety of driving range and inconvenience of battery recharging has placed high requirements on the energy efficiency of electric vehicles. To reduce driving-wheel slip energy consumption while cornering, a torque vectoring control strategy for a rear-wheel independent-drive (RWID) electric vehicle is proposed. First, the longitudinal linear stiffness of each driving wheel is estimated by using the approach of recursive least squares. Then, an initial differential torque is calculated for reducing their overall tire slippage energy dissipation. However, before the differential torque is applied to the two side of driving wheels, an acceleration slip regulation (ASR) is introduced into the overall control strategy to avoid entering into the tire adhesion saturation region resulting in excessive slip. Finally, the simulations of typical manoeuvring conditions are performed to verify the veracity of the estimated tire longitudinal linear stiffness and effectiveness of the torque vectoring control strategy. As a result, the proposed torque vectoring control leads to the largest reduction of around 17% slip power consumption for the situations carried out above. [ABSTRACT FROM AUTHOR]

This paper aims to explore torque optimization control issue in the turning of EV (Electric Vehicles) with motorized wheels for reducing energy consumption in this process. A three-degree-of-freedom (3-DOF) vehicle dynamics model is used to analyze the total longitudinal force of the vehicle and explain the influence of torque vectoring distribution (TVD) on turning resistance. The Genetic Algorithm-Particle Swarm Optimization Hybrid Algorithm (GA-PSO) is used to optimize the torque distribution coefficient offline. Then, a torque optimization control strategy for obtaining minimum turning energy consumption online and a torque distribution coefficient (TDC) table in different cornering conditions are proposed, with the consideration of vehicle stability and possible maximum energy-saving contribution. Furthermore, given the operation points of the in-wheel motors, a more accurate TDC table is developed, which includes motor efficiency in the optimization process. Various simulation results showed that the proposed torque optimization control strategy can reduce the energy consumption in cornering by about 4% for constant motor efficiency ideally and 19% when considering the motor efficiency changes in reality. [ABSTRACT FROM AUTHOR]

To improve the trackability of in-wheel motor drive (IWMD) and wheel-individual steer electric vehicles (EVs) when steering actuators fail, the fail-operation control strategy was proposed to correct vehicles in a steering failure situation and avoid losing control of vehicle steering. A linear quadratic regulator (LQR) decides the additional yaw moment of the vehicle according to vehicle state errors. The tire force estimation module estimates the compensating resistance moment generated by the failed wheel according to the tire slip angle and the vertical tire force. By isolating the failed wheel, the optimal torque distribution (OTD) controller allocates the additional yaw moment and the compensating resistance moment to normal wheels to realize the fail-operation control of the IWMD vehicle. The control effect was verified through co-simulation of MATLAB/Simulink and Trucksim. Compared with the uncontrolled and direct torque allocation methods, the proposed OTD method reduces the lateral trajectory error of the vehicle by 86% and 60.5%, respectively, when failure occurs, and achieves better velocity maintaining ability, which proves the effectiveness of the proposed fail-operation control strategy. [ABSTRACT FROM AUTHOR]

The differential drive assist steering (DDAS) system makes full use of the advantages of independent control of wheel torque of electric vehicle driven by front in-wheel motors to achieve steering assistance and reduce the steering effort of the driver, as the electric power steering (EPS) system does. However, as an indirect steering assist technology that applies steering system assistance via differential drive, its linear control algorithm, like existing proportion integration differentiation (PID) controllers, cannot take the nonlinear characteristics of the tires' dynamics into account which results in poor performance in road feeling and tracking accuracy. This paper introduces an active disturbance rejection control (ADRC) method into the control issue of the DDAS. First, the third-order ADRC controller of the DDAS is designed, and the simulated annealing algorithm is used to optimize the parameters of ADRC controller offline considering that the parameters of ADRC controller are too many and the parameter tuning is complex. Finally, the 11-DOF model of the electric vehicle driven by in-wheel motors is built, and the standard working conditions are selected for simulation and experimental verification. The results show that the ADRC controller designed in this paper can not only obviously reduce the steering wheel effort of the driver like PID controller, but also have better nonlinear control performance in tracking accuracy and smooth road feeling of the driver than the traditional PID controller. [ABSTRACT FROM AUTHOR]

The article discusses the research study conducted to examine the geodynamic setting and tectonic model of porphyry copper?gold deposits in West Junggar, northwest China in the Central Asia Orogenic Belt (CAOB). It mentions that the porphyry deposit is associated with a late carboniferous intrusive complex. It reveals that the ore-bearing porphyry system was derived from partial melting of multiple sources including oceanic crust and a subduction-modified mantle wedge in paleo-Junggar ocean.