A Hybrid Energy Storage System (HESS), consists of two or more types of energy storage technologies, mostly includingbatteries, flywheels, super-capacitors, and fuel cells. The complementary features of HESS make it outperform any single energy storage device depending on the application energy requirements in different scenarios/conditions. To overcome the opposing limitations of battery and supercapacitor, the battery has relatively high energy density but low power density as compared to the supercapacitor, an active battery/supercapacitor hybrid energy storage system (HESS) with dc/dc converter is proposed. However, the main issue with an active battery/supercapacitor HESS is current flow control to accomplish two goals: minimizing the magnitude fluctuation of current flowing in/out of the battery and minimizing energy loss experienced by the supercapacitor/s. The purpose of this article is to perform optimization of an active battery/supercapacitor HESS for a Hybrid Electric Vehicle (HEV). The HESS topology used for the optimization consists of a parallel connected battery and supercapacitor/s to drive the load through the respective dc/dc converters. In this article, an efficient multiplicative-increase-additive-decrease concept-based algorithm is used to ensure an optimal solution. MATLAB simulations are used to demonstrate that the proposed scheme can optimally minimize the magnitude fluctuation of the battery current and the SC energy loss.

Invertebrates are abundant in horticulture and farming environments, and can be detrimental. Early pest detection for an integrated pest-management system with an integration of physical, biological, and prophylactic methods has huge potential for the better yield of crops. Computer vision techniques with multispectral images are used to detect and classify pests in dynamic environmental conditions, such as sunlight variations, partial occlusions, low contrast, etc. Various state-of-art, deep learning approaches have been proposed, but there are some major limitations to these methods. For example, labelled images are required to supervise the training of deep networks, which is tiresome work. Secondly, a huge in-situ database with variant environmental conditions is not available for deep learning, or is difficult to build for fretful bioaggressors. In this paper, we propose a machine-vision-based multispectral pest-detection algorithm, which does not require any kind of supervised network training. Multispectral images are used as input for the proposed pest-detection algorithm, and each image provides comprehensive information about different textural and morphological features, and visible information, i.e., size, shape, orientation, color, and wing patterns for each insect. Feature identification is performed by a SURF algorithm, and feature extraction is accomplished by least median of square regression (LMEDS). Feature fusion of RGB and NIR images onto the coordinates of Ultraviolet (UV) is performed after affine transformation. The mean identification errors of type I, II, and total mean error surpass the mean errors of the state-of-art methods. The type I, II, and total mean errors, with 6.672% UV weights, were emanated to 1.62, 40.27, and 3.26, respectively.

Dirty surroundings impose a detrimental effect on the beauty of our environment. Hiring and paying for a large workforce to clean the debris on the roads, parks, and streets is a costly and tiring job. This project has been carried out to design and fabricate a battery-powered road-sweeper vehicle (RSV), equipped with a scrubber system. The proposed machine, equipped with scrubbers, increases the operational time while decreasing the operational cost. In contrast with typical industrial counterparts, an eco-friendly road-sweeper machine is presented in this study.