Decarbonising heavy-duty trucks is challenging due to high journey power and energy requirements. With a growing fleet of commercial vehicles in the UK, biomethane can provide significant reductions in greenhouse gas (GHG) emissions compared to fossil diesel. Methane is a potent GHG with a global warming potential (GWP) of 23–36, therefore reducing levels in the atmosphere can have a significant impact on climate change. There are a range of anthropogenic sources of methane that could be collected and processed to provide sustainable energy (upcycled), e.g., agricultural waste and the waste water system. This paper explores the impact of using upcycled methane in transport in South East England, evaluating local sources of anthropogenic methane and the environmental and economic impact of its use for a heavy-duty truck compared to fossil and battery electric alternatives. Analysis concludes that the use of upcycled methane in transport can provide significant reductions in lifecycle GHG emissions compared to diesel, fossil natural gas or battery electric trucks, and give net negative GHG emissions where avoided environmental methane emissions are considered. Furthermore, upcycling solutions can offer a lower cost route to GHG reduction compared to electrification.

In this paper, we consider an application of an online reference governor (RG) to boost pressure control of automotive diesel engines. In order to treat the system nonlinearity, the plant is expressed as a piecewise affine model through system idenfication for each fixed operating condition of engine speed and fuelling. The plant model is utilized for the future prediction in the online RG. Then, an online iterative RG algorithm based on the gradient descent method is applied to generate the optimal modified reference so that the constraint on the maximal turbocharger rotation speed is satisfied. We show the effectiveness of the present method through an experiment with a production vehicle.

We present a purely mechanical means of producing highly concentric spherical lenses at the endfaces of optical fibers. The production process has two stages. First, conical lenses are produced in a grinding process that ensures excellent concentricity. Then, the conical lenses are transformed to spherical lenses using a novel process called loose abrasive blasting. The cone grinding is carried out on a microgrinding machine, which has a sophisticated control system that enables the production of precision conical lenses. The blasting is carried out on a diamond blasting machine. Plots showing automatic centering performance of the microgrinding machine and scanning electron microscopy images of the conical and spherical lenses are presented.

This paper considers an application of reference governor (RG) to automotive diesel aftertreatment temperature control. Recently, regulations on vehicle emissions have become more stringent, and engine hardware and software are expected to be more complicated. It is getting more difficult to guarantee constraints in control systems as well as good control performance. Among model-based control methods that can directly treat constraints, this paper focuses on the RG, which has recently attracted a lot of attention as one method of model prediction-based control. In the RG, references in tracking control are modified based on future prediction so that the predicted outputs in a closed-loop system satisfy the constraints. This paper proposes an online RG algorithm, taking account of the real-time implementation on engine embedded controllers. In order to realize the online RG algorithm, the following three elements are needed: (i) a plant model to predict future behavior of the control system, (ii) an objective function that quantifies how suitable a modified reference candidate is, and (iii) an online optimization algorithm that computes the most suitable modified reference from a set of candidates. For (i), a catalyst temperature model is derived based on thermal exchanges. In regards to (ii), three objective function candidates are considered and, through simulations, one in which a constraint is characterized as a soft constraint by a barrier function that penalizes the constraint violation is chosen. Owing to the parameters in the objective function selected, the resulting transient responses of the catalyst temperature can be tuned. (iii) The optimization algorithm is realized in the RG based on the steepest descent method, which minimizes a nonlinear objective function iteratively online. Finally, an experimental result is shown in which the present RG algorithm is applied to a production vehicle controller. The result shows the applicability and effectiveness of the present method.

This paper presents a quantitative measurement of the effect of surface roughness of optical fiber end-faces on light coupling efficiency. Fibers tested included cleaved fibers and fibers with ground endfaces of varying degrees of surface roughness. The endfaces were ground on a specially built nano-grinding machine. The assessment was based on the Gaussian distribution of the intensity of light collected from both the cleaved and the ground endfaces of the fibers. Measurements of the total amount of light at the output end were also made. Results showed that light-coupling efficiency has improved dramatically with the improvement of surface finish. Surface roughness measurements were carried out using atomic force microscopy (AFM)