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Studies of diesel system deposits continue to be the subject of interest and publications worldwide. The introduction of high pressure common rail systems resulting in high fuel temperatures in the system with the concomitant use of fuels of varying solubilizing ability (e.g. ULSD and FAME blends) have seen deposits formed at the tip of the injector and on various internal injector components. Though deposit control additives (DCAs) have been successfully deployed to mitigate the deposit formation, work is still required to understand the nature and composition of these deposits. The study of both tip and internal diesel injector deposits (IDID) has seen the development of a number of bench techniques in an attempt to mimic field injector deposits in the laboratory. One of the most used of these is the Jet Fuel Thermal Oxidation Tester or JFTOT (ASTM D3241).

Hybrid electric vehicles (HEV) are essential for reducing fuel consumption and emissions. However, when analyzing different segments of the transportation industry, for example, public transportation or different sizes of delivery trucks and how the HEV are used, it is clear that one powertrain may not be optimal in all situations. Choosing a hybrid powertrain architecture and proper component sizes for different applications is an important task to find the optimal trade-off between fuel economy, drivability, and vehicle cost. However, exploring and evaluating all possible architectures and component sizes is a time-consuming task. A search algorithm, using Gaussian Processes, is proposed that simultaneously explores multiple architecture options, to identify the Pareto-optimal solutions.

The driving risk field model offers a feasible approach for assessing driving risks and planning safe trajectory in complex traffic scenarios. However, the conventional risk field fails to account for the vehicle size and acceleration, results in the same trajectories are generated when facing different vehicle types and unable to make safe decisions in emergency situations. Therefore, this paper firstly introduces the acceleration and vehicle size of surrounding vehicles for improving the driving risk model. Then, an integrated decision-making and planning model is proposed based on the combination of the novelty risk field and model predictive control (MPC), in which driving risk and vehicle dynamics constraints are taken into consideration. Finally, the multiple driving scenarios are designed and analyzed for validate the proposed model.