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This paper presents a tradeoff analysis in terms of accuracy and computational cost between different architectures of artificial neural networks for the State of Charge (SOC) estimation of lithium batteries in hybrid and electric vehicles. The considered layouts are partly selected from the literature on SOC estimation, and partly are novel proposals that have been demonstrated to be effective in executing estimation tasks in other engineering fields. One of the architectures, the Nonlinear Autoregressive Neural Network with Exogenous Input (NARX), is presented with an unconventional layout that exploits a preliminary routine, which allows setting of the feedback initial value to avoid estimation divergence. The presented solutions are compared in terms of estimation accuracy, duration of the training process, robustness to the noise in the current measurement, and to the inaccuracy on the initial estimation. Moreover, the algorithms are implemented on an electronic control unit in serial communication with a computer, which emulates a real vehicle, so as to compare their computational costs. The proposed unconventional NARX architecture outperforms the other solutions. The battery pack that is used to design and test the networks is a 20 kW pack for a mild hybrid electric vehicle, whilst the adopted training, validation and test datasets are obtained from the driving cycles of a real car and from standard profiles.

AbstractUptake of electric vehicles is accelerating as governments around the world aim to decarbonize transportation. However, swift and widespread electric vehicle (EV) adoption will require some degree of controlled charging to mitigate the adverse impacts of electric vehicle adoption. Simulating the interaction between transportation and power requires new modelling tools with operational detail and spatial-temporal granularity. This analysis evaluates the potential benefits of utility-controlled charging (UCC) with the objective of reducing variable renewable energy (VRE) curtailment in decarbonized power systems using a framework that links travel and power system models using an intermediate charging model. Results show that the addition of VRE generation infrastructure shows the most impact on electricity system operating emissions and costs, but EV charging plays a significant role as well. Within EV charging strategies, UCC charging decreases emissions by 7% compared to uncontrolled charging. UCC is proven to be most effective in the summer due to higher electric vehicle fuel economy. Finally, the type of VRE generation infrastructure on the grid may have implications for siting of EV charging infrastructure due to the typical temporal peaks of wind and solar energy. These findings demonstrate how the use of distinct but linked travel and power sector models can be deployed to reduce multi-sector emissions and costs.

Abstract The kinetics of methane hydrate decomposition was studied using a semibatch stirred-tank reactor. The decomposition was accomplished by reducing the pressure on a hydrate slurry in water to a value below the three-phase equilibrium pressure at the reactor temperature. The data were obtained at temperatures from 274 to 283 K and pressures from 0.17 to 6.97 MPa. The stirring rates were high enough to eliminate mass-transfer effects. Analysis of the data indicated that the decomposition rate was proportional to the particle surface area and to the difference in the fugacity of methane at the equilibrium pressure and the decomposition pressure. The proportionality constant showed an Arrhenius temperature dependence. An estimate of the hydrate particle diameters in the experiments permitted the development of an intrinsic model for the kinetics of hydrate decomposition.

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.

Wave energy conversion is a promising field of renewable energy, but it still faces several technological and economic challenges. One of these challenges is to improve the energy efficiency and adaptability of Wave Energy Converters to varying wave conditions. A technological approach to solve this efficiency challenge is the negative spring mechanisms illustrated in recent studies. This paper proposes and analyzes a novel negative spring technological concept that dynamically modifies the mass and inertia of a Wave Energy Converter by transferring seawater between its compartments. The added value of the presented technology relies on interoperability, ease of manufacturing and operating, and increased energy efficiency for heterogeneous sea states. The concept is presented in two analyzed alternatives: a passive one, which requires no electrical consumption and is purely based on the relative motion of the bodies, and an active one, which uses a controlled pump system to force the water transfer. The system is evaluated numerically using widely accepted simulation tools, such as WECSIM, and validated by physical testing in a wave flume using decay and regular test scenarios. Key findings include a relevant discussion about system limitations and a demonstrated increase in the extracted energy efficiency up to 12.7% while limiting the maximum power extraction for a singular wave frequency to 3.41%, indicating an increased adaptability to different wave frequencies because of the amplified range of near-resonance operation of the WEC up to 0.21 rad/s.

Research to date has suggested that both individual marine species and ecological processes are expected to exhibit diverse responses to the environmental effects of climate change. Evolutionary responses can occur on rapid (ecological) timescales, and yet studies typically do not consider the role that adaptive evolution will play in modulating biological responses to climate change. Investigations into such responses have typically been focused at particular biological levels (e.g., cellular, population, community), often lacking interactions among levels. Since all levels of biological organisation are sensitive to global climate change, there is a need to elucidate how different processes and hierarchical interactions will influence species fitness. Therefore, predicting the responses of communities and populations to global change will require multidisciplinary efforts across multiple levels of hierarchy, from the genetic and cellular to communities and ecosystems. Eventually, this may allow us to establish the role that acclimatisation and adaptation will play in determining marine community structures in future scenarios.