Browse Publications Technical Papers 2020-01-0152
2020-04-14

A Study of the Effect of Weather Data and Other Assumptions on the Calculation of MAC LCCP 2020-01-0152

Average temperatures on Earth have been on the rise due to excessive emission of man-made CO2, which includes contributions from automobiles and their air-conditioners, or mobile air-conditioners (MACs). The Improved Mobile Air Conditioner Greenhouse Gas Life Cycle Climate Performance (IMAC-GHG-LCCP) tool is used to analyze the life cycle climate performance (LCCP) of a MAC system and calculates the resultant total lifetime CO2-equivalent emissions for the vehicle. The IMAC-GHG-LCCP tool is an adaptation and improvement of the recent automotive LCCP standard, GREEN-MAC-LCCP (Global Refrigerants Energy & Environmental-MAC-LCCP) with a focus on simplicity and ease-of-use. The user has the option to choose which refrigerants to analyze, driving parameters/drive cycle, cities for comparison, and power consumption of the MAC system. The tool uses the latest Typical Meteorological Year 3 (TMY3) weather data for the emissions calculations. The manner in which the weather data is binned has a major contribution to how the lifetime CO2 emissions are calculated. Previous versions binned the average temperature for each hour in each month for each city. The newest version of the tool bins the weather data according to the number of times (frequency) the city enters the particular weather bin. The change in weather data binning has shown a significant decrease in reported CO2-equivalent emissions. On average, all the cities in the world report a 21% reduction of emissions based on the new weather binning. An updated analysis of the impact of internal heat exchangers has been included, showing the impact of updated weather data binning on the results. An additional sensitivity analysis on the tool shows that the lifetime of the vehicle, driving time, and driving distance have the largest impact on the LCCP of the MAC system. A detailed analysis of the new weather data binning and sensitivity analysis of the tool is presented in this paper.

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