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Efforts to decarbonize the heavy-duty vehicle sector have generated vast interest in transitioning from conventional diesel trucks to battery electric trucks (BETs). As a result, understanding energy consumption characteristics of BETs has become important for a variety of applications, for instance, assessing the feasibility of deploying BETs in place of conventional diesel trucks, predicting the state-of-charge (SOC) of BETs after specific duty cycles, and managing BET charging needs at the home base or en-route. For these applications, mesoscopic energy consumption models offer a good balance between the amount and fidelity of the input data needed, such as average traffic speed and road grade on a link-by-link basis, and the model performance. As a common intelligent transportation system (ITS) application, this paper presents a comparative assessment of mesoscopic energy consumption models for BETs developed using three different machine learning techniques. The results show that the random forest (RF) regression outperforms the extreme gradient boosting (XGBoost), the light gradient boosting machine (LightGBM), as well as the conventional linear regression as evidenced by the resulting model having a higher coefficient of determination (R2) value than that of its counterparts. When applied to the simulated dataset, the RF regression can capture the behaviors of BET energy consumption well where the R2 value of the resulting model is 0.94.
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Application of Wireless Charging at Seaports for Range Extension of Drayage Battery Electric Trucks
Even though heavy-duty battery electric trucks (BETs) have become commercially available, their range limitation still hinders widespread adoption. Drayage has been regarded as a suitable application for early BETs due to typically having limited daily mileage. However, drayage operation can vary widely and some form of range extension may still be needed for BETs operating in this application. In this paper, wireless charging at port terminals is proposed for this purpose. Potential wireless charging zones at port terminals are identified, and efficacy of wireless charging to extend BET range in drayage operation is verified by simulating the activity of20 BETs from a drayage operator serving the ports of Los Angeles and Long Beach, using a microscopic BET energy consumption model. Furthermore, an optimization problem is formulated for optimal wireless charging zone planning from the port authority's perspective, considering subsets of the identified zones, and charging power options to choose from, for different budget ranges. In this context, zone planning means determining which areas of the port terminals should be selected for installing wireless charging systems, and what level of charging power should be for each selected zone's system. For each budget range, the optimization problem is solved using genetic algorithm to determine an optimal zone plan that provides the maximum amount of energy through wireless charging per unit cost of installation. The results show that wireless charging can aid improving activity completion of the simulated fleet by 5%, and further optimizing the zone plan can achieve similar performance with lower cost.
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- Award ID(s):
- 2152258
- PAR ID:
- 10511104
- Publisher / Repository:
- IEEE
- Date Published:
- Journal Name:
- IEEE Transactions on Vehicular Technology
- Volume:
- 73
- Issue:
- 4
- ISSN:
- 0018-9545
- Page Range / eLocation ID:
- 4599 to 4609
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1109/TVT.2023.3344213
