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California has set a goal for all drayage trucks operating in the state to be zero-emitting by 2035. In order to achieve this goal, drayage operators would need to transition 100% of their Heets to zero-emission vehicles such as battery electric trucks (BETs). This article presents an intelligently controlled charging model for BETs that minimizes charging costs while optimizing subsequent tour completion. To develop this model, real-world activity data from a drayage truck Heet operating in Southern California was combined with a two-stage clustering technique to identify trip and tour patterns. The energy consumption for each trip and tour was then simulated for BETs with a battery capacity of 565 kWh using a 150 kW charging power level. Home base charging load profiles were generated using the proposed charging model, subject to constraints of the energy needed to complete the next subsequent tour and Time-of-Use energy cost rates. A sensitivity analysis evaluated three scenarios: a passive scenario with a 5% state-of-charge (SOC) constraint after completing the subsequent tour, an average scenario with a 50% SOC constraint, and an aggressive scenario with an 80% SOC constraint. Results indicated that the 80% SOC constraint scenario achieved the lowest charging cost. However, it also yielded the lowest tour completion rate (51%). In contrast, the 5% SOC constraint scenario registered the highest tour completion rate. These results revealed that 96% of the tours could be successfully completed using the intelligently controlled charging model. The remaining tours were infeasible, indicating that the available time at the home base was inadequate for charging the necessary energy for the next tour. In terms of total costs, the scenario with a 5% SOC constraint resulted in an annual cost of approximately $40,000, whereas the 80% SOC scenario nearly doubled that amount. 

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.