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This report describes tests of a fuel additive in a medium-duty, high-swirl, direct-injection diesel engine. The additive was found to have little influence on general combustion performance or on NOx emissions. On the other hand, it had a profound effect on particulate emissions. This was most clear under high load where particle emissions are highest. Here, when the engine was switched from running on the base fuel to the additive treated fuel, particle emissions at first increased and then fell to levels about 40% lower (by particle volume) than those initially produced by the base fuel. The additive had a long lasting effect. After running with the additive for about 25 hours, emission levels with the base fuel were only slightly higher than those with the additive treated fuel. We believe that the additive action is associated with a combination of cleaning and surface conditioning. More work should be done to understand the relative importance of these two mechanisms.

Low emission heavy-duty diesel engines are increasingly utilizing four-valve designs with vertical central injectors. However, two-valve DI diesel engines with inclined injectors offset from the centerline of the piston bowl are likely to continue to be used in medium and light duty applications for some time. In such situations, designing of the hole-type nozzle is very difficult and may cause unavoidable back-drilling problems. The purpose of this paper is to solve back-drilling problems connected with hole-type nozzles and improve fuel-air mixing which leads to more efficient combustion. Based on geometric considerations, this paper introduces single-cone hole-type nozzles, double-cone hole-type nozzles, and the critical principal angles for hole-type nozzles. The single-cone hole-type nozzles and double-cone hole-type nozzles can meet requirements for height of the spray impingement points and spray orifice distribution angle at the same time.

Because of the advantages of excellent power, fuel economy and zero-emission characteristics, electric buses have been used widely as cities’ short-range commuter vehicles. However, the high-frequency noise becomes more prominent for the powertrain system of the electric bus due to the lack of noise masking effect for the traditional internal combustion engine. To improve the noise characteristic of electric bus powertrain, the identification of the main noise source of the powertrain is well needed. In this paper, the effects of the motor and transmission on the noise level of the electric bus powertrain have been studied using lead packaging method. The variations of acoustical power level of the powertrain according to different rotation speed and torque under the conditions of only motor covered and only transmission covered have been discussed.

Electric buses have been used widely as cities' short-range commuter vehicles, because of their excellent power, fuel economy and emission characteristics. However, the lack of a noise masking effect for the traditional internal combustion engine, the high-frequency noise becomes more prominent for the powertrain system. The high-frequency noise gives people an unpleasant feeling on psychological and physiological. To control electric vehicle powertrain noise, the identification of the main noise source of the powertrain is well needed. In this paper, Empirical Mode decomposition (EMD) combined with Independent component Analysis (ICA) and continuous Wavelet transform (CWT) was used to identify the main noise source of the electric bus powertrain. The contribution of each noise source to the overall noise level was calculated and compared.

Due to the complexity and timeliness of the dual power source control system for range extended electric vehicles, a real-time predictive fuzzy energy management strategy based on speed prediction, which comprehensively takes into account the demand power of auxiliary power unit, future average speed and driving distance is proposed in this work. Firstly, to improve the topicality and accuracy of the control system, the convolutional neural network with long short-term memory neural network (CNN-LSTM) algorithm is adopted to predict the future driving speed by the speed features and adjacent speeds.