Fuel cell electric vehicles (FCEVs) have received significant attention in recent times due to various advantageous features, such as high energy efficiency, zero emissions, and extended driving range. However, FCEVs have some drawbacks, including high production costs; limited hydrogen refueling infrastructure; and the complexity of converters, controllers, and method execution. To address these challenges, smart energy management involving appropriate converters, controllers, intelligent algorithms, and optimizations is essential for enhancing the effectiveness of FCEVs towards sustainable transportation. Therefore, this paper presents emerging energy management strategies for FCEVs to improve energy efficiency, system reliability, and overall performance. In this context, a comprehensive analytical assessment is conducted to examine several factors, including research trends, types of publications, citation analysis, keyword occurrences, collaborations, influential authors, and the countries conducting research in this area. Moreover, emerging energy management schemes are investigated, with a focus on intelligent algorithms, optimization techniques, and control strategies, highlighting contributions, key findings, issues, and research gaps. Furthermore, the state-of-the-art research domains of FCEVs are thoroughly discussed in order to explore various research domains, relevant outcomes, and existing challenges. Additionally, this paper addresses open issues and challenges and offers valuable future research opportunities for advancing FCEVs, emphasizing the importance of suitable algorithms, controllers, and optimization techniques to enhance their performance. The outcomes and key findings of this review will be helpful for researchers and automotive engineers in developing advanced methods, control schemes, and optimization strategies for FCEVs towards greener transportation.

Device-free indoor localization has gained significant attention in recent years in the fields of wireless sensing and machine learning. This paper proposes an improved CSI-based device-free indoor localization (DFIL) system using a machine learning-based classification approach. The proposed system exploits the variations in wireless signals reflected from objects in the environment to locate and track targets without requiring any device to be carried by them. The system uses machine learning techniques such as random forest and support vector machines to classify the received signal strength indicator (RSSI) data into different target zones. The classification accuracy is improved by using channel state information (CSI) data alongside RSSI data. The experimental results show that the proposed system achieves higher accuracy than existing DFIL systems, with an average accuracy of 94% depending on the number of training samples. This enhanced localization method can be used in real-world settings like smart house automation systems, security, and the healthcare industry.

This case study aims to investigate the causes of low efficiency in a carton production line by calculating Overall Equipment Effectiveness (OEE). A typical carton factory has been chosen for this study, which has two production lines, namely finished goods and corrugated boards. The define, measure, analyze, improve, and control approach is applied while implementing Six Sigma principles such as Kaizen, fishbone diagrams, and 5W+1H as its systematic procedure. The analysis involves estimating four machines’ average OEE across 12 shifts. A cost-effective method is applied to resolve the problems that cause the lines to be less efficient. By applying the proposed method, the OEE becomes more efficient by 29% for finished goods and 9% for corrugated boards. Value stream mapping has been used to track the improvements. It is found that emphasizing higher OEE values enhances operational performance, leading to better efficiency, power usage, cycle time, and equipment repair.