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.

In the process of vehicle chassis electrification, different active actuators and systems have been developed and commercialized for improved vehicle dynamic performances. For a vehicle system with actuation redundancy, the integration of individual chassis control systems can provide additional benefits compared to a single ABS/ESC system. This paper describes a Unified Chassis Control (UCC) strategy for enhancing vehicle stability and ride comfort by the coordination of four In-Wheel Drive (IWD), 4-Wheel Independent Steering (4WIS), and Active Suspension Systems (ASS). Desired chassis motion is determined by generalized forces/moment calculated through a high-level sliding mode controller. Based on tire force constraints subject to allocated normal forces, the generalized forces/moment are distributed to the slip and slip angle of each tire by a fixed-point control allocation algorithm. Regarding the uneven road, H∞ robust controllers are proposed based on a modified quarter-car model. Evaluation of the overall system was accomplished by simulation testing with a full-vehicle CarSim model under different scenarios. The conclusion shows that the vertical vibration of the four wheels plays a detrimental role in vehicle stability, and the proposed method can effectively realize the tire force distribution to control the vehicle body attitude and driving stability even in high-demanding scenarios.

The collision warning system (CWS) plays an essential role in vehicle active safety. However, traditional distance-measuring solutions, e.g., millimeter-wave radars, ultrasonic radars, and lidars, fail to reflect vehicles’ relative attitude and motion trends. In this paper, we proposed a vehicle-to-vehicle (V2V) cooperative collision warning system (CCWS) consisting of an ultra-wideband (UWB) relative positioning/directing module and a dead reckoning (DR) module with wheel-speed sensors. Each vehicle has four UWB modules on the body corners and two wheel-speed sensors on the rear wheels in the presented configuration. An over-constrained localization method is proposed to calculate the relative position and orientation with the UWB data more accurately. Vehicle velocities and yaw rates are measured by wheel-speed sensors. An extended Kalman filter (EKF) is applied based on the relative kinematic model to combine the UWB and DR data. Finally, the time to collision (TTC) is estimated based on the predicted vehicle collision position. Furthermore, through UWB signals, vehicles can simultaneously communicate with each other and share information, e.g., velocity, yaw rate, which brings the potential for enhanced real-time performance. Simulation and experimental results show that the proposed method significantly improves the positioning, directing, and velocity estimating accuracy, and the proposed system can efficiently provide collision warning.

To overcome the low operation efficiency, high labour-intensiveness and high risk in the artificial live-line replacement of insulator strings, a robot for overhead transmission line maintenance was developed. In order to suppress effectively the influences of disturbance signals and uncertainties on tracking precision and stability of the robot mechanical arm motion under high voltage and strong electromagnetic interference, this paper proposed a H∞ control theory-based robust trajectory tracking control method for the robot mechanical arm. Through layering robot control architecture, a dynamic model of mechanical arm basic motion was established by the Lagrange method combined with an armature voltage equation of the joint motor, and the unified dynamic model of mechanical arm different motion was obtained. On this basis, the state-space model of mechanical arm motion error was deduced under disturbances and uncertainties, and thus an H∞ control model for mechanical arm motion was constructed. Subsequently, the H∞ controller for the mechanical arm trajectory tracking control system was solved by linear matrix inequality (LMI) based on the established model, and the asymptotic stability of the mechanical arm motion control system was verified by selecting the appropriate Lyapunov function. The proposed method for such a controller was proved to be of good versatility, strong adaptability and sound expansibility. Finally, simulation results verified the effectiveness of the H∞ controller and field operation tests further validated the engineering practicability of such a control method in macro and micro aspects.

With the increasing application of additive manufacturing in various fields, it is particularly important to monitor the manufacturing process, product quality, and printer status aiming at obtaining high-quality manufactured products. This paper introduces a kind of additive manufacturing process monitoring based on the vibration data of the fused deposition modeling(FDM). The random forest model has been applied to identify the normal state and filament jam state of the printer, which could achieve an accuracy rate of 94 %. The experimental results demonstrate that the proposed method can accurately identify the mechanical faults in the process of additive manufacturing, and effectively monitor the machine health and ensure the fabricated parts quality.