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LCA study finds connected and automated vehicle subsystems could enable net reduction in vehicle energy use and GHGs by up to 9%

Researchers at the University of Michigan and Ford Motor Company have found that using a Level 4 connected and automated vehicle (CAV) subsystem could increase vehicle primary energy use and GHG emissions by 3–20% due to increases in power consumption, weight, drag, and data transmission.

However, when potential operational effects of CAVs are included (such as eco-driving, platooning, and intersection connectivity), the net result is up to a 9% reduction in energy and GHG emissions in the base case. Their study is published in the ACS journal Environmental Science & Technology.

Credit: ACS, Gawron et al. Click to enlarge.

The major factors that will determine CAV energy outcomes include the following: eco-driving, platooning, lightweighting, and rightsizing, reduced driving to locate parking, ride sharing, congestion mitigation, higher travel demand due to reduced travel cost, increased travel by underserved populations, and faster highway speeds. When these factors are combined in a travel demand and efficiency framework with a 100% CAV adoption scenario, the resulting impact ranges from a 60% reduction in energy consumption up to a 200% increase. Integrating these results with EIA reference case projections for the transportation sector produces light duty vehicle energy consumption estimates between 7,000 and 18,000 trillion Btu in 2050 compared to 16,000 trillion Btu in 2016.

The existing literature on CAV energy implications … focuses primarily on the operational impact. However, little is known about the full life cycle implications of CAV deployment. Previous studies have omitted the potential vehicle production life cycle impacts with the rationale that they are smaller in magnitude when compared to the travel related energy consumption, but no work has been done to date to examine this assumption.

To fill the gap, this study provides a life cycle assessment (LCA) of CAV sensing and computing subsystems integrated into both internal combustion engine vehicle (ICEV) and battery electric vehicle (BEV) platforms. The LCA quantifies the burdens from the production and use of Level 4 CAV subsystems applied to the vehicle platforms.

—Gawron et al.

SAE Level 4 autonomy has the vehicle driving and monitoring the environment within defined use cases in which the human need not take back control.

For the study, the team modeled the potential effect of CAV technology on vehicle operation based on direct operational effects from the literature; only direct effects enabled by or directly related to the CAV technology adoption were included.

The team modeled its BEV platform ias the 2015 Ford Focus Electric and the ICEV platform as the 2015 Ford Focus. The small, medium, and large CAV subsystems were based on the configurations in use on the Tesla Model S, Ford Fusion (AV test vehicles), and Waymo’s Chrysler Pacifica, respectively.

The life cycle phases included in the study were materials production, manufacturing and assembly, use, and end of life management. Four factors needed to be considered for the use-phase analysis, the team noted:

  • The increased vehicle energy consumption due to the added electricity demand.

  • The increased fuel consumption due to the weight of the CAV subsystem.

  • Increased aerodynamic drag due to the use of exterior mounted components.

  • The burdens associated with map data transmission over wireless networks.

Medium CAV subsystem GHG emission (1,300 kg CO2-eq) breakdown by component. Credit: ACS, Gawron et al.Click to enlarge.

The team drew five major conclusions from their study:

  1. Due to the higher GHG burden associated with generating electricity onboard an ICEV compared to the US grid mix and the greater sensitivity of fuel consumption to increased mass for an ICEV, the CAV subsystem burden is approximately a factor of 2 less for BEVs than for ICEVs. Looking at the vehicle-level results, CAVs with electrical powertrains have 40% lower life cycle GHG emissions compared to a conventional powertrain.

  2. Wireless data transmission for live maps is a significant contributor to life cycle burdens. Limiting transmission to existing standard maps results in 35% lower GHG emissions compared to HD maps assuming transmission over a 4G LTE network.

  3. The results are highly sensitive to assumptions for the direct benefits realized in CAV operation. Achieving a 14% reduction in fuel consumption due to the operational efficiencies of CAVs produces significant environmental benefits in the baseline scenario. However, these benefits erode if only a 5% reduction is achieved. The authors noted that this result highlights the importance of eco-driving algorithms in CAV design since drive cycle smoothing can reduce life cycle GHG emissions by 7− 16% compared to typical human-controlled drive cycles.

  4. The added weight and power demand from the computing platform produces significant impacts. At 10 kg and 200 W, the computer contributes nearly half of the total CAV subsystem burden in the baseline scenario. If the power demand were instead 2,000 W, as reported for some prototype or developmental CAVs, then the contribution would rise and environmental benefits would be eliminated.

  5. Large exterior-mounted CAV components have the potential to increase aerodynamic drag significantly. The fuel consumption increase due to drag was found to contribute up to 70% of the large CAV subsystem burden, which offsets the environmental benefits. CAV technology is at an early stage of development; sensing and computing components will continue to be miniaturized and packaged more compactly, but in the near-term the size and shape of exterior-mounted equipment will have tangible impacts.

CAVs are a disruptive technology that will likely transform personal mobility and the built urban landscape in the coming decades. Impacts on life cycle energy consumption and GHG emissions from the indirect effects of CAV adoption are likely to be significant. Some effects such as shared mobility and optimized routing will decrease energy use and emissions, while other effects such as increased mobility demand will increase energy use and emissions.

—Gawron et al.


  • James H. Gawron, Gregory A. Keoleian, Robert D. De Kleine, Timothy J. Wallington, and Hyung Chul Kim (2018) “Life Cycle Assessment of Connected and Automated Vehicles: Sensing and Computing Subsystem and Vehicle Level Effects” Environmental Science & Technology doi: 10.1021/acs.est.7b04576


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