Energy Prediction for Hotel Loads in Mild-Hybrid Heavy-Duty Vehicle
Year: 2025
Type: article
Abstract: Hoteling loads in long-haul trucks consume onboard energy resources in a very inefficient way. In this paper an optimal control algorithm is developed to charge the battery pack during the drive phase up to a certain state-of- charge (SOC) level, sufficient to meet the power demands of the auxiliary load during the hotel phase. Physics-based grey-box models are developed to estimate the heating ventilation, and air-conditioning (HVAC) power con- sumption. For the other auxiliary loads, a machine learning algorithm is developed to predict the power as a time series by tracking the user activ- ity. The developed physics-based and data-driven models are validated for the experimental data of class 8 heavy-duty trucks to show their efficacy. These developed models also generate precise load profiles which are fed to the developed dynamic programming (DP) framework to generate the op- timal SOC trajectories. These models ultimately help the vehicle’s battery pack charge only up to the SOC necessary for the hotel phase during the drive time. When the vehicle is out of charge during the hotel phase, these models also help in estimating the amount of idling required to charge the battery enough to support the rest of the hotel period. This saves unneces- sary idling. As a result, a cost savings of $ 40 and CO2 reduction of 175 lb to the environment is achieved for a single heavy-duty truck.
Institutions: The Ohio State University, Eindhoven University of Technology
Language: English
Subfield: Automotive Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4413853591