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Goods movement and port related activities are a significant source of emissions in many large urban areas. Electrification of diesel cargo handling equipment is one method of reducing community exposure to these emissions, that also provides the potential for reducing greenhouse gas emissions. This study evaluated the performance of several pieces of zero emission cargo transfer equipment for a demonstration conducted at two terminal locations at the Port of Long Beach (POLB). This included the data logging of three battery-electric top handlers and one battery-electric yard tractor, as well as two baseline diesel top handlers and one diesel yard tractor. The battery-electric equipment typically operated about 5 hours per day, while using between 34 to 50% of the battery pack state of charge (SOC). In general, the battery-electric equipment was able to provide comparable hours of operation to the diesel equipment over a typical 8-hour shift.

A novel ambient dilution tunnel has been designed, tested and employed to measure the emissions from active parked regenerations of Diesel Particulate Filters (DPFs) for 2007 and 2010 certified heavy duty diesel trucks (HDDTs). The 2007 certified engine had greater regulated emissions than the 2010 certified engine. For a fully loaded 2007 DPF there was an initial period of very large mass emissions, which was then followed by very large number of small particle emissions. The Particle Size Distribution, PSD, was distributed over a large range from 10 nm to 10 μm. The parked regenerations of the 2010 DPF had a much lower initial emission pattern, but the second phase of large numbers of small particles was very similar to the 2007 DPF. The emission results during regeneration have been compared to total emissions from recent engine dynamometer testing of 2007 and 2010 DPFs, and they are much larger.

Eco-Approach and Departure (EAD) has been considered as a promising eco-driving strategy for vehicles traveling in an urban environment, where information such as signal phase and timing (SPaT) and geometric intersection description is well utilized to guide vehicles passing through intersections in the most energy-efficient manner. Previous studies formulated the optimal trajectory planning problem as finding the shortest path on a graphical model. While this method is effective in terms of energy saving, its computation efficiency can be further enhanced by adopting machine learning techniques. In this paper, we propose an innovative deep learning-based queue-aware eco-approach and departure (DLQ-EAD) system for a plug-in hybrid electric bus (PHEB), which is able to provide an online optimal trajectory for the vehicle considering both the downstream traffic condition (i.e. traffic lights, queues) and the vehicle powertrain efficiency.