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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.

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.

This paper investigates how social outcomes from urban transport projects typically play out by reflecting on multi-scale spatial changes induced by projects over time, and the extent to which such changes meet varied interests in project outcomes. We use a multi-methods case study approach using two exemplars, a metro project in London and Seoul, which established extensive public transport networks to support urban growth. Our study highlighted that urban transport network expansion does not always enhance life opportunities for all due to intermediate and cumulative impacts of spatial changes induced by projects. Immediate benefits such as enhanced accessibility were often undermined by long-term consequences of incremental spatial changes at local scales. This study also indicated that differential patterns of spatial changes around nodes between centre and periphery could be attributed to multiple negative impacts on people living in the most deprived areas. To enhance social outcomes, we suggest an integrated approach to urban transport and spatial development that focuses on scale and temporal dimensions of spatial transformation enacted by projects. In conclusion, achieving sustainable and equitable effects from urban transport infrastructure requires careful examination of broader societal consequences of long-term spatial changes and locational contexts, especially function and socio-economic conditions.

Renewable energy sources have become necessary for long-term energy sustainability due to the increased demand for electric cars and worrisome rises in carbon dioxide emissions from traditional energy sources. Furthermore, transportation is one of the sectors that uses the most energy on the planet, accounting for 24% of overall consumption. Fossil fuels are still the dominant energy source for balancing global demand/supply dynamics. Supporting laws and regulations have enhanced the first phase of environmentally friendly energy-resource consumption. This has spurred the development of new solutions that cut greenhouse-gas emissions and reduce the air pollution produced by internal combustion engines that are fuelled by fossil fuels. Wind energy is one of the clean energy sources that may be utilised for this purpose. Wind energy has been used to power electric-car-charging infrastructure, generally in a hybrid mode with another renewable source. This research examines the possibility of using wind energy as a standalone energy source to support electric-vehicle-charging infrastructure. Using data from Malacca, Malaysia, and HOMER software, the project will build and optimise a standalone wind-powered charging station. An RC-5K-A wind turbine coupled to a battery and converter is the appropriate choice for the system. The findings demonstrate that the turbine can produce 214,272 kWh per year at the cost of USD 0.081/kWh, confirming wind’s future feasibility as an energy-infrastructure support source.

Instrumented cells, equipped with miniature sensors, are proposed to aid the next stage of electrification in the automotive and aerospace industries. To optimize the energy density available within a lithium ion (li-ion) pack we demonstrate how a power line communication (PLC) network can be formed at an individual cell level. This reduces the need for complex communication cables within a vehicle wiring loom.\ud \ud Here we show a unique prototype smart cell (instrumented cell equipped with interface circuitry and processing capability) can be connected via a PLC network, to enable monitoring of vital parameters (temperature, voltage, current), regardless of cell state of charge (2.5 V to 4.2 V DC operating voltage). In this proof-of-concept study, we show the reliable system (0 errors detected over ∼24 hr experiment, acquired data (logged at 10 Hz) from cells (in a parallel configuration), and comparative data for cell internal and external temperature was recorded. During a prolonged discharge (1C, 5A discharge) a peak core temperature >3 °C hotter than surface temperature was observed, highlighting the need to understand cell operation in cooling system design.

AbstractThis paper assesses how parameter uncertainties in the model for rise velocity of CO2 droplets in the ocean cause uncertainties in their rise and dissolution in marine waters. The parameter uncertainties in the rise velocity for both hydrate coated and hydrate free droplets are estimated from experiment data. Thereafter the rise velocity is coupled with a mass transfer model to simulate the fate of dissolution of a single droplet.The assessment shows that parameter uncertainties are highest for large droplets. However, it is also shown that in some circumstances varying the temperature gives significant change in rise distance of droplets.