In this paper, an electromagnetic energy-regenerative suspension system is proposed to achieve active control and vibration energy harvesting. For this system, a PID controller based on BP neural network algorithm is designed and vehicle dynamic performances are studied. Based on the traditional energy-regenerative efficiency calculation, a novel self-supply energy efficiency concept is proposed to evaluate the utilization effect of the recycled energy for this dual-functional suspension. Simulations are carried out, and the results show that the vehicle dynamic performances are effectively improved under different input conditions, including road surfaces and vehicle speeds. Furthermore, the energy-regenerative suspension can recover part of vibration energy, where the self-supply energy efficiency is about 55% and the energy-regenerative efficiency is about 16%. Meanwhile, the BP-PID algorithm also enables the suspension system’s self-adaptability and stability characteristics on its energy recovery capability.

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For passengers, the most common feeling during running on the bumpy road is continuous vertical discomfort, and when the vehicle is braking, especially the emergency braking, the instantaneous inertia of the vehicle can also cause a strong discomfort of the passengers, so studying the comfort of the vehicle during the braking process is of great significance for improving the performance of the vehicle. This paper presented a complete control scheme for vehicles equipped with the brake-by-wire (BBW) system aiming at ensuring braking comfort. A novel braking intention classification method was proposed based on vehicle braking comfort, which divided braking intention into mild brake, medium comfort brake, and emergency brake. Correspondingly, in order to improve the control accuracy of the vehicle brake system and to best meet the driver’s brake needs, a braking intention recognizer relying on fuzzy logic was established, which used the road condition and the brake pedal voltage and its change rate as input, output real-time driver's braking intention, and braking intensity. An optimal brake force distribution strategy for the vehicle equipped with the BBW system based on slip rate was proposed to determine the relationship between braking intensity and target slip ratio. Combined with the vehicle dynamics model, improved sliding mode controller, and brake force observer, the joint simulation was conducted in Simulink and CarSim. The cosimulation results show that the proposed braking intention classification method, braking intention recognizer, brake force distribution strategy, and sliding mode control can well ensure the braking comfort of the vehicle equipped with the BBW system under the premise of ensuring brake safety.

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The contact interface variables are difficult to measure for an ultrasonic motor. When the ultrasonic motor works under different preloads, the error between the traditional efficiency model and the real output is quite large. In order to solve these two problems, we propose a novel efficiency model. It takes measured preload and the feedback voltage data as the input, which may offer better accuracy and on-line ability. Firstly, the effect of the preload on the drive characteristics is investigated, and the relationship between preload and the change in motor energy input is analyzed. Secondly, a contact model based on measured preload and feedback voltage is built, providing a more accurate description of the contact variables. Finally, an efficiency model was developed with a new composite stator structure. A preload test rig for a 60 mm ultrasonic motor is built and real operating conditions are measured. The results show that the correlation coefficient of the present model is 0.991, larger than 0.925 of the conventional model. The proposed model is more consistent with the real working conditions for the motor.