Simulation Study Using Vehicle and Engine Activity Data from 38 Class Eight Trucks to Understand EAD's Potential in Reducing Energy Consumption and Emissions.
Los Angeles, California, United States
Evaluation of Battery Electric Trucks and Connected Vehicle Technologies For Drayage Application
Summary Information
Electrifying heavy-duty trucks has the potential to reduce both energy consumption and environmental impacts. However, concerns over the range of Battery Electric Trucks (BETs) limit widespread adoption. Enhancing their energy efficiency can expand their service range and enable longer routes. The connected vehicle technology Eco-Approach and Departure (EAD) involves a traffic signal broadcasting its current signal phase and timing (SPaT) data so that approaching trucks can calculate the most energy efficient speed profile for the truck to follow until it clears the intersection. Drayage trucks operate primarily on local streets governed by signals making them primed to benefit from EAD. This studycollected vehicle and engine activity data from 38 Class Eight trucks belonging to a drayage operator in Southern California during early 2017. The primary location of this truck fleet was approximately one mile from the Port of Los Angeles. Microscopic traffic simulation was used to evaluate the effectiveness of EAD application.
METHODOLOGY
Data collection from each truck spanned one to two months and yielded over 130,000 miles and more than 15,000 hours of truck operations. To simulate the truck performance, the study specified many aspects of BETs in the simulator and designed an optimization model to recommend speed and acceleration to the truck based upon its distance to the signal and the SPaT data. The study examined a simple network which had three lanes on one leg approaching a signalized intersection. In total, 32 scenarios were run at 30 iterations each. The scenarios are designed based on 1) two technologies (Baseline, Electric Truck EAD (ETEAD)); 2) traffic volume (vehicles/hour) – 300, 600, 900, and 1200; and 3) market penetration rate (percent) – 0, 10, 20, and 100. Energy consumed was measured in kilojoules, and the benefit was calculated in a reduction between the EAD scenario versus the baseline of BETs but no EAD.
FINDINGS
In nearly all tested scenarios, ETEAD led to a reduction in energy consumption.
- Energy savings approached nearly eight percent at traffic volume of 300 veh/hr.
- Energy efficiency improvements tend to decrease as both traffic volume and penetration rate increase.
- An exception was observed at 900 veh/hr and 20 percent penetration rate, efficiency was slightly better than 900 veh/hr with 10 percent penetration rate.
Table 1 shows the percent change in energy consumption by different penetration rate and traffic volume.
Table 1. Percent Change in energy consumption by penetration rate and traffic volume
| Traffic Volume (veh/hr) | Penetration Rate (%) | Energy change (%) |
| 300 | 10 | -7.8 |
| 600 | 10 | -4.8 |
| 900 | 10 | -3.2 |
| 900 | 20 | -3.5 |
| 1200 | 10 | -2.6 |
| 1200 | 100 | +.2 |
