This article reviews the design and evaluation of different DC-DC converter topologies for Battery Electric Vehicles (BEVs) and Plug-in Hybrid Electric Vehicles (PHEVs). The design and evaluation of these converter topologies are presented, analyzed and compared in terms of output power, component count, switching frequency, electromagnetic interference (EMI), losses, effectiveness, reliability and cost. This paper also evaluates the architecture, merits and demerits of converter topologies (AC-DC and DC-DC) for Fast Charging Stations (FCHARs). On the basis of this analysis, it has found that the Multidevice Interleaved DC-DC Bidirectional Converter (MDIBC) is the most suitable topology for high-power BEVs and PHEVs (> 10kW), thanks to its low input current ripples, low output voltage ripples, low electromagnetic interference, bidirectionality, high efficiency and high reliability. In contrast, for low-power electric vehicles (<10 kW), it is tough to recommend a single candidate that is the best in all possible aspects. However, the Sinusoidal Amplitude Converter, the Z-Source DC-DC converter and the boost DC-DC converter with resonant circuit are more suitable for low-power BEVs and PHEVs because of their soft switching, noise-free operation, low switching loss and high efficiency. Finally, this paper explores the opportunity of using wide band gap semiconductors (WBGSs) in DC-DC converters for BEVs, PHEVs and converters for FCHARs. Specifically, the future roadmap of research for WBGSs, modeling of emerging topologies and design techniques of the control system for BEV and PHEV powertrains are also presented in detail, which will certainly help researchers and solution engineers of automotive industries to select the suitable converter topology to achieve the growth of projected power density.

Tea is a popular beverage all around the world, and in Bangladesh the cultivation of tea plays a vital role. Many diseases affect the proper growth of tea leaves leading to its reduction, thus hindering of the production of tea. However, if the disease is identified at an early age it would solve all the above mentioned problems through the application of appropriate treatment, or through the pruning of the diseased leaves to prevent further spread of the disease. To solve this problem image processing is the best option to detect and diagnose the disease. The main goal of this research is to develop an image processing system that can identify and classify the two most widespread tea leaf diseases in Bangladesh, namely brown blight disease and the algal leaf disease, from a healthy leaf. Disease identification is the first step; there are many methods that have been used for identifying the leaf disease. In this paper, Support Vector Machine classifier (SVM) is used to recognize the diseases. Eleven features are analyzed during the classification. These features are then used to find the most suitable match for the disease (or normality) every time an image is uploaded into the SVM database. When a new picture is uploaded into the system the most suitable match is found and the disease is recognized. The approach is novel since the number of features compared by the SVM classifier is reduced by three features compared to previous researches, without adversely sacrificing the success rate of the classifier, which retains an accuracy of more than 90%. This also speeds up the identification process, with each leaf image taking 300ms less processing time compared to previous research using SVM, thus ensuring a greater number of leaves can be processed in a given time frame. The proposed solution increases in efficiency of the detection, identification, and classification process will enable the tea industry in Bangladesh to become more competitive globally, by reducing the losses suffered due to diseases of the leaf, and thus increasing the overall tea production rate.

Long-haul heavy-duty vehicles, including trucks and coaches, contribute to a substantial portion of the modern-day European carbon footprint and pose a major challenge in emissions reduction due to their energy-intensive usage. Depending on the hydrogen fuel source, the use of fuel cell electric vehicles (FCEV) for long-haul applications has shown significant potential in reducing road freight CO${}_2$ emissions until the possible maturity of future long-distance battery-electric mobility. Fuel cell heavy-duty (HD) propulsion presents some specific characteristics, advantages and operating constraints, along with the notable possibility of gains in powertrain efficiency and usability through improved system design and intelligent onboard energy and thermal management. This paper provides an overview of the FCEV powertrain topology suited for long-haul HD applications, their operating limitations, cooling requirements, waste heat recovery techniques, state-of-the-art in powertrain control, energy and thermal management strategies and over-the-air route data based predictive powertrain management including V2X connectivity. A case study simulation analysis of an HD 40-tonne FCEV truck is also presented, focusing on the comparison of powertrain losses and energy expenditures in different subsystems while running on VECTO Regional delivery and Longhaul cycles. The importance of hydrogen fuel production pathways, onboard storage approaches, refuelling and safety standards, and fleet management is also discussed. Through a comprehensive review of the H${}_2$ fuel cell powertrain technology, intelligent energy management, thermal management requirements and strategies, and challenges in hydrogen production, storage and refuelling, this article aims at helping stakeholders in the promotion and integration of H${}_2$ FCEV technology towards road freight decarbonisation.

Power electronics converters (PECs) are responsible for efficiently converting electrical energy between power generators, storage systems and power consumers/loads. The PECs are subjected to complicated power loading factors throughout their operations and, thus, are proven to have issues regarding lifetime. Therefore, according to the original equipment manufacturers and suppliers, the reliability of the PECs is a key priority in order to be widely used and accepted in the industries. In this context, this review article aims to provide a comprehensive overview of reliability assessment strategies and testing protocols for ensuring the lifetime requirements of PECs. In addition, the paper conducts a thorough investigation of various strategies that are employed to enhance reliability during the design and useful life phase, including the analysis of different types of failure mechanisms, identification of factors contributing to failures, and assessment of techniques for estimating and regulating junction temperatures to prolong the lifetime of the system under real-life operating conditions. Furthermore, the article highlights the challenges encountered in monitoring and predicting component degradation and outlines the crucial steps for conducting a functional safety analysis. Particular emphasis is provided on summarizing the different accelerated aging tests for power electronics converters used in automotive applications, as these tests have yet to be fully covered in previous literature. This study provides an overview and guidelines for understanding the reliability of PECs and identifying potential areas for future research. Finally, the paper concludes most of the ongoing research and innovations in this area and provides insights into the future trends and challenges in enhancing the reliability of power electronics converters

The rapid growth of battery electric vehicles (BEVs) in the automotive field has led to the need for improving their drivetrain performance, mainly focusing on the extension of the battery operating range. However, the majority of performed technical assessments only consider battery state-of-charge (SoC) and depth of discharge, while neglecting the effects on lifetime and failure probability of power electronic components, more specifically the emerging wide bandgap (WBG)-based technologies. Toward fulfilling this gap, the EV market demands lifetime estimation carried out under real-life mission profile to confirm efficiency and reliable operation of EV power electronics for an extended range meeting the EVs lifetime requirements. In this regard, this study proposes a versatile experimental device-under-test setup to investigate a novel stepwise holistic system-level lifetime estimation approach for BEV drivetrains equipped with SiC interleaved bidirectional HV dc/dc converter (IBC). To this end, three different real-life mission profile use-cases are investigated in this article and they provide systematic stress-based lifetime estimation, statistical analyses, and validations in comparison. The study outcome highlights realistic information related to significant impacts of operation range and battery SoC features on the IBC lifetime from all aspects.