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This paper presents an integrated vehicle model to simulate simultaneously the driver, powertrains, chassis, body, road condition, vehicle dynamics and the Active Suspension (AS) system with/without an energy harvesting module. The developed model is used to investigate the ride comfort and influences of energy harvesting AS system on the total energy consumption of battery Electric Vehicles (EVs) relative to EVs with a passive suspension system. Preliminary simulation results show that compared to EVs with a passive suspension system, the ones with AS system improve ride comfort, up to 31% reduction of the vehicle body acceleration RMS value, with an expense of higher energy consumption. This expense can be reduced to about 2.8% when using an energy harvesting AS system. Simulation results also demonstrate that the available energy for recuperation during the AS system operation is significant in relation to the regenerative braking energy of the propulsion system, up to approx. 70% on bumpy road surfaces.

Abstract This paper provides a forecast of electricity consumption in Cyprus up to the year 2030, based on econometric analysis of energy use as a function of macroeconomic variables, prices and weather conditions. If past trends continue electricity use is expected to triple in the coming 20–25 years, with the residential and commercial sectors increasing their already high shares in total consumption. Besides this reference scenario it was attempted to assess the impact of climate change on electricity use. According to official projections, the average temperature in the Eastern Mediterranean is expected to rise by about 1 °C by the year 2030. Using our econometrically estimated model, we calculated that electricity consumption in Cyprus may be about 2.9% higher in 2030 than in the reference scenario. This might lead to a welfare loss of 15 million Euros in 2020 and 45 million Euros in 2030; for the entire period 2008–2030 the present value of costs may exceed 200 million Euros (all expressed in constant Euros of 2007). Moreover, we assessed the additional peak electricity load requirements in the future because of climate change: extra load may amount to 65–75 Megawatts (MW) in the year 2020 and 85–95 MW in 2030.

Data Availability: The data that support the findings of this study are available from the corresponding author, C. Wen, upon reasonable request. ; Deep-learning-based fault diagnosis of wind turbine has played a significant role in advancing the renewable energy industry. However, the imbalanced data sampled by the supervisory control and data acquisition systems has led to low diagnosis accuracy. Additionally, deep neural networks can encounter issues like gradient vanishing and insufficient feature learning during backpropagation when the model is too deep. This article introduces a novel approach that is based on dynamic weight loss functions to modulate unbalanced data and improve diagnostic accuracy by focusing on misclassification of a small sample number. The proposed approach employs a 1D-CNN model and an ensemble-learning-based convolution neural network (EL-CNN) to enhance diversity of models and complementarity of feature learning. The EL-CNN model addresses the problem of local features being overlooked and provides more accurate results. The effectiveness of this proposed approach is well demonstrated through experimental cases on real wind turbine pitch system fault data. Two different networks using three different loss functions and three state-of-the-art fault diagnosis models are tested, demonstrating the EL-CNN model’s superiority. ; This work was supported in part by the European Union’s Horizon 2020 Research and Innovation Programme under Grant 820776 (INTEGRADDE), the Engineering and Physical Sciences Research Council (EPSRC) of the UK, the Royal Society of the UK, the Alexander von Humboldt Foundation of Germany, the BRIEF Award of Brunel University London, the National Natural Science Foundation of China under Grant 61973209, the Natural Science Foundation of Shanghai of China under Grant 20ZR1421200, and the Capacity Building Project of Shanghai Local Colleges and Universities of China under Grant 22010501100.

Technical and economic developments in battery and fast-charging technologies could soon make fuel cell electric vehicles, which run on hydrogen, superfluous in road transport

Diesel engine exhausts from a common rail 3.0 L F1C diesel engine were analyzed at two different load conditions of the WLTC testing cycle downstream of both the diesel particulate filter (DPF) and selective catalytic reactor (SCR) to verify their effect on the characteristics of carbon particulate matter. An array of chemical, physical and spectroscopic techniques (gas chromatography coupled with mass spectrometry (GC-MS), mobility analyzer, UV-Visible absorption and fluorescence spectroscopy) was applied for characterizing polycyclic aromatic hydrocarbons (PAH), heavy aromatic compounds and soot, constituting the particulate matter (PM) sampled from the exhaust. The engine was operated in half load (HL) (188 Nm, representing the more common condition for engine in urban traffic) and full load (FL) (452 Nm, representing the best performance of the engine operation) conditions, at the same engine speed (2000 rpm). Soot formation was enhanced in HL condition, with respect to FL, but, just because of the much lower soot amount, the after-treatment systems in this last condition resulted to be less efficient in the soot abatement. Indeed, the abatement through DPF was about 40% lower in the FL condition with respect to HL condition, and any significant further concentration decrease was found after SCR, in both conditions. By contrast, PAH concentration after DPF abatement was found to be higher in the HL with respect to FL condition. A further PAH concentration decrease of about 30% was found after the SCR in the HL condition whereas in FL the reduction was only about 5-6%. Also the heavy aromatic compounds having molecular weight above the GC-MS detection limit (300 u), were mitigated by SCR. Therefore, SCR did not cause a further soot reduction, whereas it was effective in largely reducing PAH and heavy aromatics emissions, especially in the lower temperature condition featuring the half-load condition, when combustion efficiency is worse. Moreover, SCR system reduced the emission of small particles probably due to an enhanced agglomeration of particles, with beneficial effect on the harmfulness to human health.

Optical system design has a significant impact on solar power plant efficiency. It can be advantageous to be able to very precisely adapt mirror shapes in order to improve energy yield. In this article it is investigated whether it is feasible to use non-uniform rational Beziers splines (NURBS) to describe mirrors. Algorithms and equations to evaluate optical performance by ray tracing are lined out. A parameterization scheme is given that reduces the degrees of freedom to a level suitable for automatic processing. To prove feasibility, a secondary reflector based on a NURBS surface and a compound parabolic concentrator (CPC) are designed for a specific test application and compared to each other. It is found that the suggested design method is feasible on contemporary personal computers and that the NURBS based surface performs well compared to the CPC.

AbstractDespite the rapid increase in the performance of perovskite solar cells (PSC), they still suffer from low lab‐to‐lab or people‐to‐people reproducibility. Aiming for a universal condition to high‐performance devices, we investigated the morphology evolution of a composite perovskite by tuning annealing temperature and precursor concentration of the perovskite film. Here, we introduce thermal annealing as a powerful tool to generate a well‐controlled excess of PbI2 in the perovskite formulation and show that this benefits the photovoltaic performance. We demonstrated the correlation between the film microstructure and electronic property and device performance. An optimized average grain size/thickness aspect ratio of the perovskite crystallite is identified, which brings about a highly reproducible power conversion efficiency (PCE) of 19.5 %, with a certified value of 19.08 %. Negligible hysteresis and outstanding morphology stability are observed with these devices. These findings lay the foundation for further boosting the PCE of PSC and can be very instructive for fabrication of high‐quality perovskite films for a variety of applications, such as light‐emitting diodes, field‐effect transistors, and photodetectors.

The search for affordable high performance electrode materials in photoelectrochemical hydrogen production by solar water splitting is an ongoing quest. Hematite is a photoanode material with an electronic band gap suitable for efficient absorption of visible light in a photoelectrochemical cell (PEC). Although its poor electronic structure makes hematite a controversial candidate for PEC, it remains promising because it is an earth abundant, chemically stable and low cost material – necessary prerequisites for PEC to become a competitive cost-efficient solar fuel economy. In addition to reviewing some recent PEC research on hematite and its relevant physical and chemical characteristics, we show how hematite obtained by a low cost synthesis can be refined by hydrothermal treatment and further functionalized by coating with phycocyanin, a light harvesting protein known for photosynthesis in blue-green algae.