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Active chassis dynamics control has become increasingly important in recent years for achieving high-level automated driving of intelligent electric vehicles. To improve tracking performance of the commonly-used model predictive control (MPC) in extreme scenarios, such as emergency lane changing, an improved MPC motion stability controller (MPC_ISMC) based on sliding mode variable structure active steering control and differential torque yaw control is proposed. Firstly, an MPC-based trajectory tracking controller is designed to track the desired trajectory. Subsequently, active front-wheel steering (AFS) and direct yaw moment (DYC) controllers are designed by using the backstepping sliding mode variable structure and the adaptive super-twisting sliding mode variable structure, respectively. Moreover, a cooperative control law for the AFS and the DYC is formulated by the smooth switching function and intervention intensity factor with respect to vehicle stable state and trajectory tracking error. After that, a torque distribution scheme of four wheels is optimized to achieve the additional yaw moment target. Finally, the effectiveness of the proposed cooperative control strategy is validated using a hardware-in-the-loop platform. The results indicate that the proposed controller has better performance in trajectory tracking and vehicle stability under extreme conditions compared with traditional MPC and other methods. [ABSTRACT FROM AUTHOR]

With the help of several in-wheel motors, four-wheel independent-drive electric vehicle (4WIDEV) has tremendous potential to improve vehicle performance. Except for the theme of stability enhancement during the past two decades, energy saving topic becomes more attractive recently. However, it is difficult to achieve stability and economy performance simultaneously. Given the commonly used control method of rigid yaw rate tracking may limit the energy saving potential of 4WIDEV in cornering. Therefore, be different from previous studies which often focus on a sole target, in this paper, a driving energy management strategy for 4WIDEV based on multi-objective online optimization of four-wheel torque distribution is proposed in this paper, which includes weighted yaw rate tracking error into its parameterized control objectives besides electric drive system efficiency, tire slip losses and wheel torque ripple. Meanwhile, for better coordination of stability and economy in different situation, fuzzy logic control is adopted to dynamically adjust the weight of the control objective indexes of the proposed multi-objective online optimization function. Then, a particle swarm optimization algorithm is adopted to solve the optimization function. Finally, the proposed strategy for energy-efficient driving is verified based on the built Simulink and CarSim co-simulation model. The results show that the proposed four-wheel torque distribution strategy based on the multi-objective online optimization is more effective than average distribution strategy and offline optimization strategy. [Display omitted] • Propose a driving energy management strategy based on multi-objective online optimization. • Integrate yaw rate error, in-wheel motor efficiency, and tire slip into optimization function. • Design a weight coefficient fuzzy controller to better coordinate the stability and economy. [ABSTRACT FROM AUTHOR]

Due to the actual operation of the wind turbine, the collected fault data sets are limited, and it is difficult to realize fault diagnosis through the correlation between variables. To solve this problem, a fault diagnosis method based on virtual expansion and spherical mapping model is proposed in this paper. Firstly, Hermite interpolation is applied to discrete wind power data samples to obtain an interpolation curve about the characteristics of the sample, and a synchronous sampling method is adopted for the interpolation curve to construct a virtual sample. Then, the features of the virtual sample are mapped to a three-dimensional space. Define the spherical data model and perform spherical fitting in a three-dimensional coordinate system. Finally, feature extraction is performed on the fitted spherical surface for training and testing extreme learning machine (ELM). The distribution law of fault data in the spherical model is summarized. Using the data generated based on the Bootstrap method as a control group, comparative experiments were carried out in back-propagation neural network (BP), Probabilistic Neural Network (PNN), General Regression Neural Network (GRNN), and Support Vector Machine (SVM), which verified the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper develops a four-degree-of-freedom dynamics model with fractional order based on an anti-roll connected hydro-pneumatic suspension of a four-axle vehicle. A comprehensive evaluation function determines the optimal order. The effects of the excitation parameters and the suspension's internal parameters on the system's nonlinear dynamics are investigated under sinusoidal superimposed white noise excitation. According to the simulation analysis, the system gradually stabilizes as the excitation amplitude and frequency increase. The flow coefficient variation does not affect the system. The initial pressure shows the system's chaotic and transient periodic motion in the study interval. By establishing a double power convergence sliding film variable structure controller to control the suspension, the chaos is effectively suppressed by comparing the before and after control. • In order to express the stiffness and damping force of hydro-pneumatic spring more accurately, a four-degree-of-freedom dynamic model with fractional order is established in this paper, and the best order is determined by comprehensive evaluation function. • The effects of excitation amplitude and frequency, flow coefficient and initial pressure on the motion state of the system are analyzed by means of nonlinear dynamics. • A dual-power convergent sliding mode variable structure controller is established to control the suspension and effectively suppress the chaotic vibration. [ABSTRACT FROM AUTHOR]

This paper presents a new analytical calibration method for energy management strategy designed for a plug-in hybrid electric vehicle. This method improves the actual calibration efficiency to reach a compromise among the conflicting calibration requirements (e.g. emissions and economy). A comprehensive evaluating indicator covering emissions and economic performance is constructed by using a radar chart method. A radial basis functions (RBFs) neural network model is proposed to establish a precise model among control parameters and the comprehensive evaluation indicator. The optimal Latin hypercube design is introduced to obtain the experimental data to train the RBFs neural network model. And multi-island genetic algorithm is used to solve the optimization model. Finally, an offline calibration example is conducted. Results validate the effectiveness of the proposed calibration approach in improving vehicle performance and calibration efficiency. [ABSTRACT FROM AUTHOR]

The power-split hybrid electric system is currently the most promising hybrid system. This study proposes a multi-factor integrated parametric design method for a power-split hybrid electric bus (HEB). This method considers the power source efficiency (i.e. the engine, motor and generator), transmission efficiency, vehicle driving cycle requirements, and primary control strategy. The design method is then applied to a specified city bus. System efficiency is analyzed, and the control strategy is determined. Finally, the parametric design method is validated on the co-simulation platform of Matlab/Simulink and Cruise. Results show that the fuel economy of the power-split HEB that adopted the proposed parametric design is 36% better than that of the conventional city bus. [ABSTRACT FROM AUTHOR]

• LIBS assisted with laser-stimulated absorption (LSA-LIBS) was proposed to reduce self-absorption in the presence of spectral interference. • Rational selection of the excitation line was proved to be an important factor for self-absorption reduction. • The self-absorption factor of the Ni I 341.47 nm line was reduced by 85% using LSA-LIBS. Spectral interference and self-absorption seriously affect the accuracy of quantitative analysis in laser-induced breakdown spectroscopy (LIBS). In this work, we used LIBS assisted with laser-stimulated absorption (LSA-LIBS) to solve these problems. Taking nickel (Ni) in the alloy structural steel as an example, the results show that the spectral interference of Ni lines was eliminated, the self-absorption factor of the Ni I 341.47 nm line was reduced by 85%, and the average relative error for determination of Ni element was reduced by 83% using LSA-LIBS. The results indicate that spectral interference and self-absorption can be reduced simultaneously in LSA-LIBS. [ABSTRACT FROM AUTHOR]