Energy Prediction for Hotel Loads in Mild-Hybrid Heavy-Duty Vehicle

Long-haul truck drivers are mandated to take off-duty time of 10 h (a.k.a. hoteling) before driving. During the hotel phase, drivers spend time inside their trucks (sleeper cabs) and idle the internal combustion engine for comfort by utilizing the heating, ventilation, air-conditioning (HVAC), and other onboard appliances. For one 10-h period, the average cost is about $40, which can be a lot when considering a million truck drivers idling overnight. SuperTruck II is a 48 V mild-hybrid heavy-duty truck with auxiliary loads powered by an onboard battery pack. 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. This article captures the research done to predict energy consumption in a mild-hybrid heavy-duty sleeper truck during hoteling. Physics-based gray box models are developed to estimate the power consumption of an electronically controlled compressor. For other auxiliary loads, a machine learning algorithm is developed to predict the power as a time series by tracking the user activity. The developed physics and data-driven models are validated with experimental data from heavy-duty trucks to show their efficacy. These validated models generate precise load profiles fed to the developed dynamic programming framework to generate the optimal SOC trajectories. These models help the vehicle 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 unnecessary 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 with a sleeper cab during the hoteling phase.