China 6 moving average window method for real driving emission evaluation: Challenges, causes, and impacts

Area of Science:

• Environmental Science
• Automotive Engineering
• Regulatory Compliance

Background:

• The China 6 real driving emission (RDE) calculation employs a complex light-duty moving average window (MAW) method with various boundaries.
• Previous studies indicated potential underestimation by the MAW method, but systematic analysis of the causes was lacking.

Purpose of the Study:

• To quantitatively analyze the underestimation problem, its causes, and impacts within the light-duty MAW method for China 6 RDE.
• To propose and utilize the instantaneous utilization factor (IUF) for a deeper understanding of MAW calculation dynamics.

Main Methods:

• Conducted real-world driving tests with 29 vehicles across 10 cities.
• Applied different calculation boundaries and analyzed data exclusion and extended factors.
• Introduced and employed the instantaneous utilization factor (IUF) for cause analysis.

Main Results:

• Over 75% of tests showed underestimation by the MAW method, with some reaching 100%.
• Data exclusion was found to cause biased MAW results; removing extended factors significantly reduced bias.
• MAW method leads to lower IUF at test start/end, particularly concerning when cold-start data is included.

Conclusions:

• The coupled effects of data exclusion, extended factors, and window characteristics necessitate simultaneous consideration for method improvement.
• The current drift-check process is insufficient for monitoring portable emission measurement systems (PEMS) during tests.
• Inaccurate MAW results can lead to implausible emission limits, inventories, and policies.