Chuan Wang

More and more attention has been paid to ships with a DC power grid. State-of-charge (SOC) estimation is a pivotal and challenging assignment for lithium-ion batteries in such ships. However, the precision of SOC estimation is strongly connected with the system parameters. To better identify these parameters in lithium-ion batteries, a differential evolution (DE) algorithm was introduced into this paper as the optimizer. Initially, a first-order RC equivalent circuit model (ECM) was created to characterize the battery’s dynamic behavior. Following this, to estimate open-circuit voltage (OCV) throughout the entire dynamic process, a math model of optimization was established to minimize inaccuracies between the real and estimated terminal voltages. Moreover, estimated SOC values were obtained through OCV-SOC mappings and were contrasted against the true SOC values. The findings manifested the efficacy of the presented structure and technique in comparison with various frequently-cited DE variants.

Marine mammals relying on tactile perception for hunting are able to achieve a remarkably high prey capture rate without visual or acoustic perception. Here, a self-powered triboelectric palm-like tactile sensor (TPTS) is designed to build a tactile perceptual system for underwater vehicles. It is enabled by a three-dimensional structure that mimics the leathery, granular texture in the palms of sea otters, whose inner neural architecture provides additional clues indicating the importance of tactile information. With the assistance of palm structure and triboelectric nanogenerator technology, the proposed TPTS has the ability to detect and distinguish normal and shear external load in real-time and approximate the external stimulation area, especially not affected by the touch frequency, that is, it can maintain stable performance under high-frequency contact. The results show that the TPTS is a promising tool for integration into grippers mounted on underwater vehicles to complete numerous underwater tasks.

The human–machine interface plays an important role in the diversified interactions between humans and machines, especially by swaping information exchange between human and machine operations. Considering the high wearable compatibility and self-powered capability, triboelectric-based interfaces have attracted increasing attention. Herein, this work developed a minimalist and stable interacting patch with the function of sensing and robot controlling based on triboelectric nanogenerator. This robust and wearable patch is composed of several flexible materials, namely polytetrafluoroethylene (PTFE), nylon, hydrogels electrode, and silicone rubber substrate. A signal-processing circuit was used in this patch to convert the sensor signal into a more stable signal (the deviation within 0.1 V), which provides a more effective method for sensing and robot control in a wireless way. Thus, the device can be used to control the movement of robots in real-time and exhibits a good stable performance. A specific algorithm was used in this patch to convert the 1D serial number into a 2D coordinate system, so that the click of the finger can be converted into a sliding track, so as to achieve the trajectory generation of a robot in a wireless way. It is believed that the device-based human–machine interaction with minimalist design has great potential in applications for contact perception, 2D control, robotics, and wearable electronics.

With the growing demand for emission reductions and fuel efficiency improvements, alternative energy sources and energy storage technologies are becoming popular in a ship microgrid. In order to balance the two non-compatible objectives, a new differential evolution variant, which is named as SaCIDE-r, was proposed to solve the optimization problem. In this algorithm, a Collective Intelligence (CI) based mutation operator was proposed by mixing some promising donor vectors in the current population. Besides, a self-adaptive mechanism which was developed to avoid introducing extra control parameters. Further, to avoid being trapped in local optima, a re-initialization mechanism was developed. Then, we have evaluated the performances of the proposed SaCIDE-r approach by studying some numerical optimization problems of Congress on Evolutionary Computation (CEC) 2013 with D = 30, compared with seven stateof-the-art DE algorithms. Moreover, the proposed SaCIDE-r method was applied for economic scheduling of a shipboard microgrid under different cases compared with other multi-objective optimizing methods, resulting in very competitive performances. The comprehensive experimental results have demonstrated that the presented SaCIDE-r method might be a feasible solution for such a kind of optimization problem.

In this paper, a Self-learning Collective Intelligence Differential Evolution (SLCIDE) algorithm was proposed to optimize both the architecture and parameters of a Feedforward Neural Network (FNN). In order to improve the exploration-exploitation capability, a new Collective Intelligence (CI) based mutation operator was proposed by mixing some promising donor vectors in the current population. Besides, a self-learning mechanism which was designed to adaptively select m top ranked donor vectors was developed by using a widely used unsupervised learning method, k-means. As a result, the proposed approach can be more adaptive and statistically powerful on versatile problems. Then, we evaluated the performances of the proposed SLCIDE approach by studying some numerical optimization problems of CEC 2013 with D = 30 and D = 50. Further, the proposed SLCIDE method was applied to train a FNN on four most popular datasets, resulting in very competitive performances. The comprehensive experimental results have demonstrated that the presented SLCIDE method obtain better results compared with other state-of-the-art algorithms.

Due to the existence of line impedances and low-bandwidth communication, the traditional peer-to-peer control method based on droop control has difficult meeting the requirements of current sharing and voltage stability in islanded DC microgrids at the same time. In this paper, a novel current-sharing control strategy based on injected small ac voltage with low frequency and low amplitude is proposed for multiple paralleled DC–DC converters. The small ac voltage is superimposed onto the output voltage of each converter. Then, the reactive circulating power is generated and used to regulate the output DC voltage of each converter. Under the droop characteristic between the injected frequency and output DC current, a feedback mechanism is generated to realize the accurate current sharing. On this basis, a reactive power-voltage limiter link and virtual negative impedance are added. Under the interaction of the two links, the bus voltage drop caused by line impedances can be almost completely eliminated. This method does not need any communication or to change the hardware structure. The controller design process is presented in detail along with a system stability analysis. Finally, the feasibility and effectiveness of the proposed control strategy are validated by the results obtained from simulations and experiments.