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Abstract In this paper, a novel energy management strategy with the improved adaptability to various conditions for plug‐in hybrid electric vehicle (PHEV) is proposed. The control parameters, derived from the benchmark test, are optimized offline for different driving conditions. The optimized parameters are implemented according to different driving behaviours identified online. The offline and online cooperation improves performance of energy management strategy in different driving conditions. Three main efforts have been made: Firstly, the valuable features that describe different driving conditions are extracted by random forest (RF) and the features are used for determining driving condition categories, utilized for online driving condition identification by support vector machine (SVM). Secondly, the control thresholds in the developed control strategy are optimized by whale optimization algorithm (WOA) under different driving conditions. The optimal control thresholds for different driving conditions will be called online after certain traffic condition is categorized. At last, simulation‐based evaluation is performed, validating the enhanced performance of the proposed methods in energy‐saving in different driving conditions.

AbstractThis paper introduces a novel optimisation algorithm for hybrid railway vehicles, combining a non‐linear programming solver with the highly efficient “Mayfly Algorithm” to address a non‐convex optimisation problem. The primary objective is to generate efficient trajectories that enable effective power distribution, optimal energy consumption, and economical use of multiple onboard power sources. By reducing unnecessary load stress on power sources during peak time, the algorithm contributes to lower maintenance costs, reduced downtime, and extended operational life of these sources. The algorithm's design considers various operational parameters, such as power demand, regenerative braking, velocity and additional power requirements, enabling it to optimise the energy consumption profile throughout the journey. Its adaptability to the unique characteristics of hybrid railway vehicles allows for efficient energy management by leveraging its hybrid powertrain capabilities.

Electric vehicles (EV) have gained global attention due to increasing oil prices and rising concerns about transportation-related urban air pollution and climate change. While mass adoption of EVs has several economic and environmental benefits, large-scale deployment of EVs on the low-voltage (LV) urban distribution networks will also result in technical challenges. This paper proposes a simple and easy to implement single-phase EV charging coordination strategy with three-phase network supply, in which chargers connect EVs to the less loaded phase of their feeder at the beginning of the charging process. Hence, network unbalance is mitigated and, as a result, EV hosting capacity is increased. A new concept, called Maximum EV Hosting Capacity (HCmax) of low voltage distribution networks, is introduced to objectively assess and quantify the enhancement that the proposed phase-shifting strategy could bring to distribution networks. The resulting performance improvement has been demonstrated over three real UK residential networks through a comprehensive Monte Carlo simulation study using Matlab and OpenDSS tools. With the same EV penetration level, the under-voltage probability was reduced in the first network from 100% to 54% and in the second network from 100% to 48%. Furthermore, percentage voltage unbalance factors in the networks were successfully restored to their original values before any EV connection. 46807 46796 0,775 3,745 Q1 Q1 SCIE

Fully electric ships have been widely developed, investigated and evaluated by the maritime industry as a potential solution to respond to the emissions control required according to the International Maritime Organization (IMO). This study aims at presenting a novel approach to evaluate the safety level of a battery-powered high speed catamaran. Following the Formal Safety Assessment procedure, the risk assessment of the considered ship was conducted leading to the identification of the involved hazards along with the estimation of their frequency and consequences thus allowing for the identification of the most severe hazards. Fault tree analysis is carried out for and the identified top events followed by an event tree analysis to estimate the risk and safety level of the vessel. Furthermore, a cost-benefit assessment is conducted to evaluate the financial impact of selected risk control options. The derived results indicate that the application of battery power systems for high speed ferries exhibits low and acceptable accident frequencies. It is also supported the current regulation to carry out mandatory risk assessment for battery-powered ships.

Many governments have begun to adopt aggressive targets for electric vehicles. However, studies of the drivers of electric vehicle (EV) adoption are scarce. Social media interactions can provide a new data-driven vantage point to explore such drivers. This study uses data from 36,000 public posts on Facebook to investigate intersectionality in EV-communication as per the Political, Economic, Social, Technological, Legal and Environmental (PESTLE) categories. A computational social science methodology was adopted using a mixed-method application of social network analysis and machine learning-based topic modelling through Latent Dirichlet Allocation algorithm on a 600,000-text corpus extracted from the Facebook posts. Results showed that political, economic, and legal posts had dense clusters around the technology policy of EV, the institutional discourse of electrification of the federal vehicle fleet, and tax and credit framework politics. The environmental and social dimensions had a higher discourse for social justice, clean air, and better health and well-being. A market shift towards EV as a service industry was observed in the technology and economics-related posts. These findings can help policymakers, and planners design contextualised energy policy for influencing EV adoption in the U.S. and other countries.

Using illustrations from some of Latin America's largest cities, the chapter examines the causes and consequences of urban expansion and the more recent phenomenon of urban sprawl. It reflects on the role of transport and mobility, both as consequences of urban growth and as causes of it. It reflects on the equity and sustainability dimensions of this phenomenon.

Coral bleaching continues to be one of the most devastating and immediate impacts of climate change on coral reef ecosystems worldwide. In 2015, a major bleaching event was declared as the "3rd global coral bleaching event" by the United States National Oceanic and Atmospheric Administration, impacting a large number of reefs in every major ocean. The Red Sea was no exception, and we present herein in situ observations of the status of coral reefs in the central Saudi Arabian Red Sea from September 2015, following extended periods of high temperatures reaching upwards of 32.5°C in our study area. We examined eleven reefs using line-intercept transects at three different depths, including all reefs that were surveyed during a previous bleaching event in 2010. Bleaching was most prevalent on inshore reefs (55.6% ± 14.6% of live coral cover exhibited bleaching) and on shallower transects (41% ± 10.2% of live corals surveyed at 5m depth) within reefs. Similar taxonomic groups (e.g., Agariciidae) were affected in 2015 and in 2010. Most interestingly, Acropora and Porites had similar bleaching rates (~30% each) and similar relative coral cover (~7% each) across all reefs in 2015. Coral genera with the highest levels of bleaching (>60%) were also among the rarest (<1% of coral cover) in 2015. While this bodes well for the relative retention of coral cover, it may ultimately lead to decreased species richness, often considered an important component of a healthy coral reef. The resultant long-term changes in these coral reef communities remain to be seen.