Energy Prediction in Heavy Duty Long Haul Trucks
| Autor: | Khuntia, Satvik |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: |
Engineering
Automotive Engineering Statistics Systems Design Sustainability Transportation Artificial Intelligence Mechanical Engineering 'Heavy Duty truck SuperTruck Hoteling hybrid idling Physics based modeling HVAC Vapor compression cycle moving boundary method heat exchanger cabin Machine Learning User activity behavior time allocation matrix transition matrix markov chain driver comfort freight efficiency RNN LSTM powertrain cooling radiator thermostat' |
| Druh dokumentu: | Text |
| Popis: | Truck drivers idle their trucks for their comfort in the Cab. They might need air conditioning to maintain a comfortable temperature and use the onboard appliances like TV, radio, etc. while they rest during their long journeys. On average idling requires 0.8 gallons of diesel per hour for an engine and up to 0.5 gallons per hour for a diesel APU. For a journey greater than 500 miles, a driver rests for 10 hours for every 11 hours of driving. Drivers tend to leave the truck idling throughout the 10 hours. With today’s cost of diesel in the US, for one 10-hour period, the average cost incurred by the owner only on idling is $32. About a million truck drivers idle their trucks overnight for more than 300 days a year. Super Truck II is a 48V mild hybrid class 8 truck with all auxiliary loads powered purely by the battery pack. This offers an opportunity to reduce the idling from the whole 10 hours to whatever is necessary to charge the battery enough to power the auxiliaries. To quantify this “necessary idling” during the hoteling period we need to predict what the power load requirement in the future would be. The total power estimation is divided into two portions, (1) Cabin Hotel loads except HVAC and (2) HVAC load. A physics-based grey box models are developed for components in the vapor compression cycle and cabin using system dynamics which is used to estimate the HVAC power consumption. A special kind of Recurrent Neural Network (RNN) called Long, and Short Term Memory (LSTM) is used to predict the cabin hotel loads by user activity tracking. Synthetic load profiles are synthesized to overcome the limitation of lack of availability of data, about the user activity inside the cabin for training the LSTM algorithm, using rules and observations derived from the existing load profile for the hotel period from a survey conducted for SuperTruck project and literature survey on driver sleeping behavior. Dynamic Time Warping along with pointwise Euclidian distance is used to quantify the accuracy of the model. This information along with the drive cycle information is provided to a Dynamic Programming (DP) algorithm which gives the baseline optimal operating points for the engine and the electric motor. This DP algorithm gives the best instances to idle the vehicle and tracks the optimal SOC trajectory to be followed by the vehicle during the hotel period and gives the best instances to switch between modes of driving. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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