Electrifying the ground vehicle is a promising approach to alleviating the energy and climate issue. The adoption of electric vehicle (EV) and hybrid electric vehicle (HEV) has introduced significant impacts on various areas of the vehicle design, especially on the chassis system. When vehicles tend to be lighter, smarter, and autonomous, more electro-actuators with wide-bandwidth control and energy regeneration capability are deployed instead of traditional hydraulic ones to improve the vehicle dynamic and energy performance. In this paper, an overview of the chassis electrification technologies regarding different configurations of electric/hybrid powertrain, active suspension and steering systems, along with a comprehensive review of key research fields on the design and control of discussed systems, is presented. For related technologies, the comparative discussion and design considerations serve as a reference for practical research and development. Although the electric solutions in different subsystems show great potential to benefit the essential chassis functions further, a fully electrified chassis still has a long way to go considering the technical maturity and priceperformance trade-off. The paper also highlights the technical gaps and supplies future directions for the chassis electrification.

Ride comfort and road holding ability of distributed drive electric vehicles are worsened due to large unsprung mass introduced by wheel side drive system. To diminish this large unsprung mass negative effects, three approaches ("Suspension and Reducer Integrated" Close-to-Wheel Drive System, "Dynamic Vibration Absorber" Close-to-Wheel Drive System and in-wheel drive system with "Active and Energy Regenerative Suspension") are introduced. The corresponding dynamics are analyzed and the schemes are provided. Evaluations of suspension performance show the superiorities of these approaches in different magnitude compared with the conventional configuration which is equipped with hub motor and passive suspension. Overall comparisons between three approaches are carried out and the feasibilities are discussed. With proper design systems.

Accurate and efficient prediction of the future trajectories of surrounding vehicles is of utmost importance in motion planning for autonomous driving. The ability to predict longer-term trajectories provides valuable information for effective motion planning. Numerous studies have contributed to the prediction of long-term vehicle trajectories. However, it is important to note that longer-term predictions can potentially lead to a trade-off between accuracy and computational complexity. In this work, we propose a structural Informer method, which can achieve accurate and efficient long-term trajectory prediction of the target vehicle. Specifically, the proposed method considers not only the temporal and spatial features of the interaction vehicle trajectory, but also the impact of vehicle state changes on the trajectory. To reduce computational redundancy and complexity while improving memory usage and prediction accuracy, the ProbSparse self-attention mechanisms and attention distillation operations are employed. The method is validated and evaluated using the NGSIM dataset, and the results demonstrate that the proposed structural Informer achieves satisfactory accuracy and time cost in long-term prediction of the TV compared with state-of-the art methods Note to Practitioners —The motivation of this research is to address the impact of future trajectories of surrounding vehicles on the motion planning of autonomous vehicles. The method proposed applies advanced deep learning methods, and its strongpoint is that the proposed network can achieve higher efficiency and accuracy of trajectory prediction compared with state-of-the art methods. The specific implementation method is to use structured embedding methods and networks to extract more valuable features of the target vehicle, such as spatiotemporal features and vehicle state features. The novel attention mechanism is designed to solve the problem of exponential growth in computational complexity of traditional attention mechanisms in long-term prediction. The advancement of the method proposed confirmed by verification on naturalistic driving dataset.

In this study, a novel multi-objective reliability-based design optimization (MORBDO) method considering the maximum allowable deviation range of design variables is proposed for the reducer housing of electric vehicles. First, the numerical model of the reducer housing is established by ABAQUS and verified by experiments. A radial basis function (RBF) neural network model is used to construct the approximate finite element model. The structural parameters of the RBF are optimized using the heuristic global optimization ability of the particle swarm optimization (PSO) algorithm. Sequential quadratic programming (SQP) and non-dominated sorting genetic algorithm II (NSGA II) are used to perform the MORBDO. Finally, the technique for order preference by similarity to ideal solution, a multi-criteria decision-making (MCDM) method, is used to select the ideal design in multi-objective Pareto points. The optimization method generated a set of Pareto non-dominated solutions with three objectives, which can be selected for a more feasible scheme using MCDM. The proposed method comprehensively measures the requirements of manufacturing and performance criteria, and the optimization results provide a variety of optimization design schemes for the reducer housing of electric vehicles.

To facilitate intelligent vehicles in making informed decisions and plans, the precise and efficient prediction of vehicle trajectories is imperative. However, the future trajectory of a vehicle is not solely determined by its own historical path; it is also influenced by neighboring vehicles (NVs). Hence, understanding the interactions between vehicles is crucial for trajectory prediction. Additionally, the computational challenges posed by long sequence time-series forecasting (LSTF) add complexity to trajectory prediction tasks. This paper introduces a novel network, named Sparse Attention Graph Convolution Network (SAGCN), designed to comprehensively consider the trajectory interaction details of multiple vehicles, optimizing the LSTF for the target vehicle (TV). Specifically, grounded in real-world driving scenarios and vehicle interaction nuances, a multi-vehicle topology graph is formulated to amalgamate the historical trajectories of the TV and the interaction trajectories of NVs. The SAGCN network employs the Graph Convolutional Network (GCN) to assimilate and analyze diverse features within the multi-vehicle topology graph, subsequently computing the future trajectory of the vehicle through a sparse attention mechanism. The proposed method is validated and evaluated using natural datasets. The results demonstrate that, in comparison to state-of-the-art methods, the SAGCN network presented attains exceptional prediction accuracy and satisfactory time efficiency when predicting the trajectories of TV in LSTF.

Electromagnetic actuated active suspension (EAAS) benefits energy harvesting while providing active control. However, the inertia of the actuator introduces an equivalent mass coupled with the sprung and unsprung mass. In addition, the specific structure of the suspension features structure non-linearity, which results in the perturbation of the equivalent mass of the actuator, the variation of the transmission ratio of the actuator output torque to the actuator force at the wheel side and an extra force to be compensated with. A dynamic model of active control considering the equivalent mass and the structure non-linearity is proposed. Based on a gearbox type EAAS, respective non-linearity is studied. For multi-objective optimization, a robust controller is designed with proper weighting functions. A virtual prototype of the EAAS is built and simulated with a bump/pothole and random excitation road profiles. Results show that neglecting the structure non-linearity effects influences the accuracy of active control. The investigation of this paper provides a fundamental methodology for the control design of actual applications of EAASs.