This paper proposes a piezoelectric energy harvester to supply power for low-consumption devices in intelligent vehicle wheels. The harvester is embedded in the spoke and possesses the ability of self-tuning due to the centrifugal force in the rotational motion. A dynamic model suitable for design is derived through the Euler-Lagrange method and verified by experiments. In order to facilitate the design of the harvester, the concept of design factor is proposed and discussed. The wheel's rotational characteristics is measured by a road test and a harvester is designed for the R16 wheel. An experimental platform with Macro Fiber Composite is built and the output power of 0.61-6.28 mW is achieved at the wheel speeds from 40 to 120 kph on real road test. The results demonstrate that the proposed piezoelectric energy harvester can be applied to establish a wireless self-sufficient intelligent vehicle wheel system.

This paper proposes the hybrid adaptive genetic algorithm (HAGA) as an improved method for solving the NP-hard two-dimensional rectangular packing problem to maximize the filling rate of a rectangular sheet. The packing sequence and rotation state are encoded in a two-stage approach, and the initial population is constructed from random generation by a combination of sorting rules. After using the sort-based method as an improved selection operator for the hybrid adaptive genetic algorithm, the crossover probability and mutation probability are adjusted adaptively according to the joint action of individual fitness from the local perspective and the global perspective of population evolution. The approach not only can obtain differential performance for individuals but also deals with the impact of dynamic changes on population evolution to quickly find a further improved solution. The heuristic placement algorithm decodes the rectangular packing sequence and addresses the two-dimensional rectangular packing problem through continuous iterative optimization. The computational results of a wide range of benchmark instances from zero-waste to non-zero-waste problems show that the HAGA outperforms those of two adaptive genetic algorithms from the related literature. Compared with some recent algorithms, this algorithm, which can be increased by up to 1.6604% for the average filling rate, has great significance for improving the quality of work in fields such as packing and cutting.

This paper proposes a time-space domain correlation-based method for gas leakage detection and location. It acquires the propagated signal on the skin of the plate by using a piezoelectric acoustic emission (AE) sensor array. The signal generated from the gas leakage hole (which diameter is less than 2 mm) is time continuous. By collecting and analyzing signals from different sensors’ positions in the array, the correlation among those signals in the time-space domain can be achieved. Then, the directional relationship between the sensor array and the leakage source can be calculated. The method successfully solves the real-time orientation problem of continuous ultrasonic signals generated from leakage sources (the orientation time is about 15 s once), and acquires high accuracy location information of leakage sources by the combination of multiple sets of orientation results. According to the experimental results, the mean value of the location absolute error is 5.83 mm on a one square meter plate, and the maximum location error is generally within a ±10 mm interval. Meanwhile, the error variance is less than 20.17.

Piezoelectric energy harvesting is a promising way to develop self-sufficient systems. Structural design and parameter optimization are key issues to improve the performance in applications. This paper presents a magnetic coupled piezoelectric energy harvester to increase the output and bandwidth. A lumped parameter model considering the static position is established and various modes are simulated. This paper focuses on the “Low frequency repulsion mode”, which is more practical. The experiment platform is built with the Macro Fiber Composite (MFC) material, and the results are consistent with the analytical simulation. The optimization process of some key parameters, such as magnets spacing and flux density, is carried out. The results show that there is a corresponding optimal spacing for each flux density, which is positive correlated. With the optimized parameter design, the system achieves peak electrical power of 3.28 mW under the harmonic excitation of 4 m/s². Compared with the conventional single cantilever harvester, the operated bandwidth is increased by 66.7% and the peak output power is increased by 35.0% in experiment.