With the development of electric vehicles, the energy harvesters of vehicle’s suspensions have been widely investigated. However, most of the existing energy harvesters have replaced the original damper of the suspensions, which reduces vehicle safety. This paper proposes a magnetic energy-harvesting suspension (MEHS) for vehicles, with the advantages of high vehicle safety and a compact structure, which consists of a passive suspension and a harvester. In this paper, the working principle and the structure of the MEHS are introduced. To better analyze the magnetic field, the magnetic flux circuit model is established by combining the magnetic field division method with the magnetic equivalent circuit model. Furthermore, a dynamic model for a two-degree-of-freedom quarter-vehicle with the MEHS is built to analyze the energy-harvesting characteristics and the damping characteristics. A prototype of the quarter-vehicle that is considering the tire stiffness, is designed and manufactured, and the performances are investigated by simulations and experiments with different frequencies, external resistances and amplitudes. The experimental results indicate that the harvested peak power of the MEHS is 1.08 W with a sinusoidal input of 10 Hz and 2 mm. Moreover, the maximum energy-harvesting efficiency of the prototype reaches 67% at the sinusoidal input of 6 Hz and 2 mm. The harvested energy of the MEHS can power some electrical equipment of vehicles.

Under extreme working conditions such as high-speed driving on roads with a large road surface unevenness coefficient, turning on a road with a low road surface adhesion coefficient, and emergency acceleration and braking, a vehicle’s stability deteriorates sharply and reduces ride comfort. There is extensive existing research on vehicle active suspension control, trajectory tracking, and control methods. However, most of these studies focus on conventional operating conditions, while vehicle stability analysis under extreme operating conditions is much less studied. In order to improve the stability of the whole vehicle under extreme operating conditions, this paper investigates the stability of a vehicle under extreme operating conditions based on linear quadratic regulator (LQR) control. First, a seven degrees of freedom (7-DOF) dynamics model of the whole vehicle is established based on the use of electromagnetic active suspension, and then an LQR controller of the electromagnetic active suspension is designed. A joint simulation platform incorporating MATLAB and CarSim was built, and the CarSim model is verified by real vehicle tests. Finally, the stability of the vehicle under four different ultimate operating conditions was analyzed. The simulation results show that the root mean square (RMS) values of body droop acceleration and pitch angle acceleration are improved by 57.48% and 28.81%, respectively, under high-speed driving conditions on Class C roads. Under the double-shift condition with a low adhesion coefficient, the RMS values of body droop acceleration, pitch acceleration, and roll angle acceleration are improved by 58.25%, 55.41%, and 31.39%, respectively. These results indicate that electromagnetic active suspension can significantly improve vehicle stability and reduce driving risk under extreme working conditions when combined with an LQR controller.

With the rapid development of Internet of Things (IoT) and the popularity of wireless sensors, using internal permanent or rechargeable batteries as a power source will face a higher maintenance workload. Therefore, self-powered wireless sensors through environmental energy harvesting are becoming an important development trend. Among the many studies of energy harvesting, the research on rotational energy harvesting still has many shortcomings, such as rarely working effectively under low-frequency rotational motion or working in a narrow frequency band. In this article, a rotational magnetic couple piezoelectric energy harvester is proposed. Under the low-frequency excitation (16 Hz), it can solve the condition that the piezoelectric beam cannot respond in time by frequency down-conversion. Therefore, the energy harvester still has a certain degree of energy harvesting ability (18–22 Hz and 29–31 Hz) under high-frequency conditions. Meanwhile, corresponding theoretical analyses and experimental verifications were carried out to investigate the dynamic characteristics of the harvester with different excitation and installation directions. The experimental results illustrate that the proposed energy harvester has a wider working bandwidth benefiting from the frequency up-conversion mechanism and frequency down-conversion mechanism. In addition, the forward beam will have a wider bandwidth than the inverse beam due to the softening effect. In addition, the maximum powers of the forward and inverse beams at 310 rpm (15.5 Hz) are 93.8 μW and 58.5 μW, respectively. The maximum powers of the two beams at 420 rpm (21 Hz) reached 177 μW and 85.2 μW, respectively. The self-powered requirement of micromechanical systems can be achieved. Furthermore, this study provides the theoretical and experimental basis for rotational energy harvesting.

The hybrid Weibull distribution model can describe the failure rules of electromechanical products more accurately than the single Weibull distribution model, and it can improve the accuracy of reliability analysis. However, the hybrid Weibull distribution model is also more complex, and the multi-parameter estimation is more difficult. In this paper, a reliability mathematical model based on the two-fold three-parameter hybrid Weibull distribution model was established, an EM optimization algorithm was derived for its solution, and a practical initial parameter selection scheme was designed. The validity of the model and the algorithm were verified, and goodness-of-fit tests were conducted through an arithmetic example. The results showed that the initial value selection scheme proposed in this paper and the corresponding solution algorithm could solve all the parameters and weight coefficients to be estimated for each sub distribution, and the obtained failure probability fitting curve had a high fit with the actual sample data, which effectively solved the multi-parameter estimation problem of the multiple mixed Weibull distribution model.