• Energy Research
• 2021-2025close
• 7. Clean energy close
• 13. Climate action close
• 1. No poverty close
• Transport Research close

The Box–Jenkins model is a polynomial model that uses transfer functions to express relationships between input, output, and noise for a given system. In this article, we present a Box–Jenkins linear model for a lithium-ion battery cell for use in electric vehicles. The model parameter identifications are based on automotive drive-cycle measurements. The proposed model prediction performance is evaluated using the goodness-of-fit criteria and the mean squared error between the Box–Jenkins model and the measured battery cell output. A simulation confirmed that the proposed Box–Jenkins model could adequately capture the battery cell dynamics for different automotive drive cycles and reasonably predict the actual battery cell output. The goodness-of-fit value shows that the Box–Jenkins model matches the battery cell data by 86.85% in the identification phase, and 90.83% in the validation phase for the LA-92 driving cycle. This work demonstrates the potential of using a simple and linear model to predict the battery cell behavior based on a complex identification dataset that represents the actual use of the battery cell in an electric vehicle.

In response to the pressing need for transportation decarbonization, this paper examines the often overlooked domain of inland waterway transport and seeks to answer which alternative fuel or power source is the most promising for that sector. As the shipping industry significantly contributes to global carbon emissions, it has been shifting towards alternative fuels and decarbonization measures in the effort to reduce them, whereas the inland waterways, operating predominantly on diesel engines, have not achieved equivalent substantial progress. Employing a systematic literature review and regional analysis, this study identifies notable trends. LNG initially emerged as a favored alternative fuel, but recent studies emphasize a shift towards “greener” solutions like batteries and hydrogen. Europe and Asia lead in these developments. This investigation uncovers critical gaps in research and development, particularly in the Northern European countries that have extensive inland waterway networks. It also calls for future studies to explore the performance of vessels that have adopted LNG compared to other emerging alternatives and emphasizes the importance of considering the time lag between technology development and research publication.

The COVID-19 pandemic has impacted all sectors of the tourism industry, particularly air transportation. However, air transport remains an important contributor to economic growth globally. Thus, this study examines whether air transport (a proxy for tourism) stimulates economic growth to validate the air-transportation-led growth hypothesis (ALGH) in the Australian context. To conduct the study, we analyse the asymmetric long-run and short-run impacts of the air passengers carried (a proxy for tourism) on the gross domestic product (GDP) in Australia. We use the nonlinear autoregressive distributed lag (NARDL) modelling approach on data for Australia from 1971 to 2019. We also examined the effects of selected control variables (i.e., energy consumption, financial development, socialisation, and urbanisation) on economic growth. In both the short and long run, we observed statistically significant asymmetric impacts of air transport on economic growth. The positive shocks in air transport propel the long-term growth of Australia’s economy. Additionally, according to the findings, negative shocks of air transport have a stronger detrimental impact on economic development than positive shocks.

Wave power is a potential technology for generating sustainable renewable energy. Several types of wave energy converters (WECs) have been proposed for this purpose. WECs operate in a harsh maritime environment that sets strict limitations on how and when the device can be economically and safely reached for maintenance. Thus, to ensure profitable energy generation over the system life cycle, system reliability is a key aspect to be considered in WEC development. In this article, we describe a reliability analysis approach for WEC development, based on the use of reliability block diagram (RBD) modelling. We apply the approach in a case study involving a submerged oscillating wave surge converter device concept that utilizes hydraulics in its power take-off system. In addition to describing the modelling approach, we discuss the data sources that were used for gathering reliability data for the components used in a novel system concept with very limited historical or experimental data available. This includes considerations of the data quality from various sources. As a result, we present examples of applying the RBD modelling approach in the context of WEC development and discuss the applicability of the approach in supporting the development of new technologies.

The intense growth of cities affects their inhabitants to a considerable extent. The issues facing the traveling population include congestion and growing harmful emissions. Urban transport requires changes towards eco-friendly solutions. However, even though new forms of traveling (sharing services) are being implemented, their integration with public transport remains problematic. On account of the large number of available services combined with the absence of their integration, city inhabitants are faced with the dilemma of choosing between one or several transport modes which would enable them to make the given trip. The main goal of this article is to propose a model for integration of different transport services which could support those who intend to travel in the decision-making process. Therefore, the parameters of a model of urban sharing services were identified and classified. The parameters discussed in the paper with reference to an extensive literature review describe how individual sharing services are functioning. What has also been identified is the location-specific factors as well as those related to the potential area of operation which affect the integration with public transport. In order to take all the relevant parameters into account and find a solution to the problem at hand, a multi-criteria decision-making approach has been proposed. To this end, scores and weights determining their impact on the model have been established. For purposes of the solution in question, the relevant calculations were conducted by referring to an actual need to travel between selected locations.

A reasonable plan for charging stations is critical to the widespread use of electric vehicles. In this paper, we propose an optimal planning method for electric vehicle charging stations. First of all, we put forward a forecasting method for the distribution of electric vehicle fast charging demand in urban areas. Next, a new mathematical model that considers the mutual benefit of electric vehicle users and the power grid is set up, aiming to minimize the social cost of charging stations. Then, the model is solved by the Voronoi diagram combined with improved particle swarm optimization. In the end, the proposed method is applied to an urban area, simulation results demonstrate that the proposed method can yield optimal location and capacity of each charging station. A contrasting case is carried out to verify that improved particle swarm optimization is more effective in finding the global optimal solution than particle swarm optimization.

Abstract Cost-effectively decarbonising the power sector and household energy use using variable renewable energy will require that electricity consumption becomes much more flexible and responsive to constraints in supply and the distribution network. In recent years residential demand response (DR) has received increasing attention that has sought to answer, based on current evidence, questions about how much consumers will engage with DR. This paper critically reviews the evidence base for residential consumer engagement with DR and draws out several important limitations in it. We argue for a more action-oriented focus on developing practical strategies to enable and unlock greater loadshifting and consumer engagement with DR within a changing technology and regulatory context. A number of recommendations are put forward for accelerating UK consumer engagement with DR, presented under three broad strategies: (a) promote awareness of smart tariffs, smart meters and storage and automation behind-the-meter devices as mutually-supportive components within a common ‘DR technology cluster’; (b) deliver targeted support for adoption of electric vehicles and other storage and automation technologies; (c) enable and support informed adoption of DR-enabling products and services through ‘smarter’ digital comparison tools (DCTs), data portability, and faster, simpler switching. The interdependency between components within this DR technology cluster delivers efficiency but also poses a risk that one delayed component (e.g., smart metering) will hold-up policy and industry support for other components. The urgency of decarbonisation goals makes it necessary to push forward as many of these elements as possible rather than the pace being set by the slowest.