As the battery provides the entire propulsion power in electric vehicles (EVs), the utmost importance should be ascribed to the battery management system (BMS) which controls all the activities associated with the battery. This review article seeks to provide readers with an overview of prominent BMS subsystems and their influence on vehicle performance, along with their architectures. Moreover, it collates many recent research activities and critically reviews various control strategies and execution topologies implied in different aspects of BMSs, including battery modeling, states estimation, cell-balancing, and thermal management. The internal architecture of a BMS, along with the architectures of the control modules, is examined to demonstrate the working of an entire BMS control module. Moreover, a critical review of different battery models, control approaches for state estimation, cell-balancing, and thermal management is presented in terms of their salient features and merits and demerits allowing readers to analyze and understand them. The review also throws light on modern technologies implied in BMS, such as IoT (Internet of Things) and cloud-based BMS, to address issues of battery safety. Towards the end of the review, some challenges associated with the design and development of efficient BMSs for E-mobility applications are discussed and the article concludes with recommendations to tackle these challenges.

Climate change and the major threat it poses to the environment and human lives is the major challenge the world faces today. To overcome this challenge, it is recommended that future automobiles have zero carbon exhaust emissions. Even though battery electric vehicles reduce carbon emissions relative to combustion engines, a carbon footprint still remains in the overall ecosystem unless the battery is powered by renewable energy sources. The proton exchange membrane fuel cell (PEMFC) is an alternate source for automotive mobility which, similar to battery electric vehicles, has zero carbon emissions from its exhaust pipe. Moreover, the typical system level efficiency of a PEMFC is higher than an equivalent internal combustion powertrain. This review article covers the background history, working principles, challenges and applications of PEMFCs for automotive transportation and power generation in industries. Since the performance of a PEMFC is greatly influenced by the design of the anode and cathode flow channels, an in-depth review has been carried out on different types of flow channel designs. This review reveals the importance of flow channel design with respect to uniform gas (reactant) distribution, membrane proton conductivity, water flooding and thermal management. An exhaustive study has been carried out on different types of flow channels, such as parallel, serpentine, interdigitated and bio-inspired, with respect to their performance and applications.

The spindle rotational accuracy is one of the important issues in a machine tool which affects the surface topography and dimensional accuracy of a workpiece. This paper presents a machine-vision-based approach to radial error measurement of a lathe spindle using a CMOS camera and a PC-based image processing system. In the present work, a precisely machined cylindrical master is mounted on the spindle as a datum surface and variations of its position are captured using the camera for evaluating runout of the spindle. TheCircular Hough Transform(CHT) is used to detect variations of the centre position of the master cylinder during spindle rotation at subpixel level from a sequence of images. Radial error values of the spindle are evaluated using the Fourier series analysis of the centre position of the master cylinder calculated with the least squares curve fitting technique. The experiments have been carried out on a lathe at different operating speeds and the spindle radial error estimation results are presented. The proposed method provides a simpler approach to on-machine estimation of the spindle radial error in machine tools.

Modern leather industries are focused on producing high quality leather products for sustaining the market competitiveness. However, various leather defects are introduced during various stages of manufacturing process such as material handling, tanning and dyeing. Manual inspection of leather surfaces is subjective and inconsistent in nature; hence machine vision systems have been widely adopted for the automated inspection of leather defects. It is necessary develop suitable image processing algorithms for localize leather defects such as folding marks, growth marks, grain off, loose grain, and pinhole due to the ambiguous texture pattern and tiny nature in the localized regions of the leather. This paper presents deep learning neural network-based approach for automatic localization and classification of leather defects using a machine vision system. In this work, popular convolutional neural networks are trained using leather images of different leather defects and a class activation mapping technique is followed to locate the region of interest for the class of leather defect. Convolution neural networks such as Google net, Squeeze-net, RestNet are found to provide better accuracy of classification as compared with the state-of-the-art neural network architectures and the results are presented.Graphical Abstract