7 research outputs found

    Greenhouse Gas and Noxious Emissions from Dual Fuel Diesel and Natural Gas Heavy Goods Vehicles.

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    Dual fuel diesel and natural gas heavy goods vehicles (HGVs) operate on a combination of the two fuels simultaneously. By substituting diesel for natural gas, vehicle operators can benefit from reduced fuel costs and as natural gas has a lower CO2 intensity compared to diesel, dual fuel HGVs have the potential to reduce greenhouse gas (GHG) emissions from the freight sector. In this study, energy consumption, greenhouse gas and noxious emissions for five after-market dual fuel configurations of two vehicle platforms are compared relative to their diesel-only baseline values over transient and steady state testing. Over a transient cycle, CO2 emissions are reduced by up to 9%; however, methane (CH4) emissions due to incomplete combustion lead to CO2e emissions that are 50-127% higher than the equivalent diesel vehicle. Oxidation catalysts evaluated on the vehicles at steady state reduced CH4 emissions by at most 15% at exhaust gas temperatures representative of transient conditions. This study highlights that control of CH4 emissions and improved control of in-cylinder CH4 combustion are required to reduce total GHG emissions of dual fuel HGVs relative to diesel vehicles.The authors would like to acknowledge support from the UK Engineering and Physical Sciences Research Council (EP/K00915X/1), the UK Department for Transport, the Office for Low Emission Vehicles and Innovate UK (project reference: 400266) and the industrial partners of the Centre for Sustainable Road Freight.This is the final version of the article. It first appeared from the American Chemical Society via http://dx.doi.org/10.1021/acs.est.5b0424

    Energy-efficient automated driving: effect of a naturalistic eco-ACC on a following vehicle

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    The optimal control problem of eco-driving, driving a vehicle using a minimal amount of fuel or electrical energy, has received much attention in the intelligent vehicles literature with many recent proposals for eco-driving adaptive cruise control systems (eco-ACC). In this paper, we consider a recentlyintroduced ‘naturalistic’ eco-ACC approach, which was designed to give human-like behaviour in vehicle following. For this eco-ACC, we show that in car following and start-stop traffic scenarios, the eco-ACC benefits not only the ego vehicle but also a further following vehicle. To see if further reductions to total energy consumption are possible, we extend the eco-ACC system with an optimal control formulation that also minimises energy losses of the following vehicle assuming it behaves according to the intelligent driver model (IDM). This gives some minor reductions in energy usage but surprisingly, for a follower that behaves according to the IDM, the naturalistic eco-ACC appears to be nearly optimal for the problem of minimising total energy loss of both the ego vehicle and its follower.</p

    Model-free road friction estimation using machine learning

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    Four different machine learning methods (a convolutional neural network, a shallow neural network, a long short-term memory network and an ensemble of bagged decision trees) were trained on simulation data to provide model-free estimates of tyre-road friction properties using readily available sensor signals. The convolutional neural network and shallow neural network had the best performance on a previously unseen ensemble of test data. When typical noise was added to the predictors’ input values, the accuracy of the predictions decreased. To avoid this, the predictors were re-trained on noisy data, making them much more robust to noisy input data and showed marked improvement in root mean square error (RMSE) performance. Again, the convolutional neural network and shallow neural network had the best performance. This shows that building a model-free tyre-road friction predictor is possible and can yield promising results.</p

    Estimating friction coefficient using generative modelling

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    It is common to utilise dynamic models to measure the tyre-road friction in real-time. Alternatively, predictive approaches estimate the tyre-road friction by identifying the environmental factors affecting it. This work aims to formulate the problem of friction estimation as a visual perceptual learning task. The problem is broken down into detecting surface characteristics by applying semantic segmentation and using the extracted features to predict the frictional force. This work for the first time formulates the friction estimation problem as a regression from the latent space of a semantic segmentation model. The preliminary results indicate that this approach can estimate frictional force
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