A model predictive controller splits battery energy among propulsion, cabin heating, and battery preconditioning, allowing a cold, low-charge autonomous EV to arrive at a charger with the battery ready for fast charging.
Assessing Geographical and Seasonal Influences on Energy Efficiency of Electric Drayage Trucks
1 Pith paper cite this work. Polarity classification is still indexing.
abstract
The electrification of heavy-duty vehicles is a critical pathway towards improved energy efficiency of the freight sector. The current battery electric truck technology poses several challenges to the operations of commercial vehicles, such as limited driving range, sensitivity to climate conditions, and long recharging times. Estimating the energy consumption of heavy-duty electric trucks is crucial to assess the feasibility of the fleet electrification and its impact on the electric grid. This paper focuses on developing a model-based simulation approach to predict and analyze the energy consumption of drayage trucks used in ports logistic operations, considering seasonal climate variations and geographical characteristics. The paper includes results for three major container ports within the United States, providing region-specific insights into driving range, payload capacity, and charging infrastructure requirements, which will inform decision-makers in integrating electric trucks into the existing drayage operations and plan investments for electric grid development.
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Model Predictive Control-Based Optimal Energy Management of Autonomous Electric Vehicles Under Cold Temperatures
A model predictive controller splits battery energy among propulsion, cabin heating, and battery preconditioning, allowing a cold, low-charge autonomous EV to arrive at a charger with the battery ready for fast charging.