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Evaluating the impact of electrification on emissions in refrigerated transport.

Number: 2122

Author(s) : DHUMANE R., STINSON M., SRICHAI R.

Summary

The transportation sector is responsible for more than 70% of petroleum consumption in the United States (US). It is currently the largest source of US greenhouse gas (GHG) emissions, accounting for roughly one-third of all domestic emissions. The demand for refrigerated transport has been steadily growing over the past decade. The cold chain has
depended on refrigerated transport units primarily running on fossil fuel-based vehicles for the reliable delivery of food, medicines, and perishables, which is critical to sustenance, health, and economic growth. As a result, fleet operators are in the process of evaluating different zero-emission transport refrigeration unit product offerings to ensure they
meet their delivery and cost targets. The current article introduces a methodology to estimate energy consumption by refrigeration systems with integrated power sources like diesel engine or battery. These systems are commonly used to control the temperature of cargo boxes used to transfer temperature sensitive products in trucks, trailers, ships and trains. A data-driven machine learning model is used to calculate the energy consumption of a trip in this tool, as it can meet both the speed and accuracy requirements. Training and test data for the machine learning models are generated by simulating the performance of a diesel-powered unit controlled by an ON-OFF controller with hysteresis, using a physics-based model in the Modelica language. The data is fitted using three different models: Ordinary Least Squares (OLS), Kriging (KRG), and Neural Network (NN). A trade-off study is conducted to compare the accuracy and complexity of the three models, revealing that the KRG model is the most suitable for the application. The R2 score of each of the models on the test data for fuel consumption are 0.96, 0.98, and 0.97, respectively. The symmetric mean absolute percentage error for these models is 18%, 11%, and 18%, respectively. A typical 8-hour delivery in different weather zones is simulated with the model to identify total fuel consumption and total power consumption. The article also demonstrates how the
impact of electrification of these systems from diesel engine powered refrigeration systems (DP-RS) to battery powered refrigeration systems (BP-RS) in various regions of the US can be calculated. By using the electric grid information, the emissions required for charging the BP-RS in each zone are calculated. The article concludes with a discussion of the impact of electrification of the fleet on GHG emissions in different zones and provides recommendations for the coefficient of performance.

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Pages: 9 p.

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  • Original title: Evaluating the impact of electrification on emissions in refrigerated transport.
  • Record ID : 30032984
  • Languages: English
  • Source: 2024 Purdue Conferences. 20th International Refrigeration and Air-Conditioning Conference at Purdue.
  • Publication date: 2024/07/17

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