Assessing vehicle fuel efficiency using a dense network of CO₂ observations

Transportation represents the largest sector of anthropogenic CO2 emissions in urban areas. Timely reductions in urban transportation emissions are critical to reaching climate goals set by international treaties, national policies, and local governments. Transportation emissions also remain one of the largest contributors to both poor air quality (AQ) and to inequities in AQ exposure. As municipal and regional governments create policy targeted at reducing transportation emissions, the ability to evaluate the efficacy of such emission reduction strategies at the spatial and temporal scales of neighborhoods is increasingly important. However, the current state of the art in emissions monitoring does not provide the temporal, sectoral, or spatial resolution necessary to track changes in emissions and provide feedback on the efficacy of such policies at a neighborhood scale. The BErkeley Air Quality and CO2 Network (BEACO2N) has previously been shown to provide constraints on emissions from the vehicle sector in aggregate over a ~1300 km2 multi-city spatial domain. Here, we focus on a 5 km, high volume, stretch of highway in the SF Bay area. We show that inversion of the BEACO2N measurements can be used to understand two factors that affect fuel efficiency: vehicle speed and fleet composition. The CO2 emission rate of the average vehicle (g/vkm) are shown to vary by as much as 27 % at different times of a typical weekday because of changes in vehicle speed and fleet composition. The BEACO2N-derived emissions estimates are consistent to within ~3 % of estimates derived from publicly available measures of vehicle type, number, and speed, providing direct observational support for the accuracy of the Emissions FACtor model (EMFAC) of vehicle fuel efficiency.

Multilane Spatiotemporal Trajectory Optimization Method (MSTTOM) for Connected Vehicles

It is agreed that connected vehicle technologies have broad implications to traffic management systems. In order to alleviate urban congestion and improve road capacity, this paper proposes a multilane spatiotemporal trajectory optimization method (MSTTOM) to reach full potential of connected vehicles by considering vehicular safety, traffic capacity, fuel efficiency, and driver comfort. In this MSTTOM, the dynamic characteristics of connected vehicles, the vehicular state vector, the optimized objective function, and the constraints are formulated. The method for solving the trajectory problem is optimized based on Pontryagin’s maximum principle and reinforcement learning (RL). A typical scenario of intersection with a one-way 4-lane section is measured, and the data within 24 hours are collected for tests. The results demonstrate that the proposed method can optimize the traffic flow by enhancing vehicle fuel efficiency by 32% and reducing pollutants emissions by 17% compared with the advanced glidepath prototype application (GPPA) scheme.

Reduction of CO2 Emissions and Cost Analysis of Ultra-Low Viscosity Engine Oil

This study is focused on the reduction of CO2 emissions and costs associated with ultra-low viscosity (ULV) engine oils for passenger vehicles. Specifically, the reduction in life cycle CO2 (LCCO2) emissions from lower-viscosity engine oil and the oil drain interval (ODI) extension were estimated taking into account both mineral engine oil and synthetic engine oil. Furthermore, the cost-effectiveness of ULV engine oils were investigated by performing base-stock cost analysis. When the volatility limit of the Noack test (American Society for testing and materials (ASTM) D5800) was set to 15 wt %, the results indicated that the lower limit of kinematic viscosity at 100 °C (KV100) for mineral engine oil (with Group-III base-stock) and synthetic engine oil (with polyalphaolefin (PAO) base-stock) were approximately 5.3 and 4.5 mm2/s, respectively. Compared to conventional 0W-16 mineral engine oil (KV100 6.2 mm2/s), the effect of reducing LCCO2 emissions on ULV mineral engine oil (ULV-Mineral, KV100 5.3 mm2/s) was estimated at 0.6%, considering 1.5–1.8 L gasoline engines in New European Driving Cycles (NEDC) mode. ULV-Mineral, which continues to use a mineral base-stock, is considered highly cost-effective since its cost is similar to the conventional 0W-16 mineral engine oil. On the other hand, compared with ULV-Mineral, the vehicle fuel efficiency improvement from the use of ULV synthetic engine oil (ULV-PAO, KV100 4.5 mm2/s) was estimated to be 0.5%. However, considering CO2 emissions during engine oil production, the reduction of LCCO2 emission from ULV-PAO compared with ULV-Mineral was estimated to be only 0.1% or less using 2030 standards (assuming a vehicle fuel efficiency of 66.5 g-CO2/km) when ODI is set equivalent (7500 km) to mineral engine oil. As a result, ULV-PAO’s cost-effectiveness, considering the cost increase of PAO base-stock, was found to be nominal. Contrariwise, when the characteristics of PAO base-stock with higher oxidation stability are used comparatively with the mineral base-stock while extending the ODI to 15,000 km, the effect of reducing LCCO2 emissions of ULV-PAO was estimated to be 0.7% in 2030, making ULV-PAO a competitive and cost-effective alternative. In other words, the popularization of synthetic engine oil toward 2030 will require the consideration of both viscosity reduction and ODI extension.