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A Hybrid Thermal Bus for Ground Vehicles Featuring Parallel Heat Transfer Pathways

SAE International Journal of Commercial Vehicles

Clemson Unversity-Shervin Shoai Naini, Junkui (Allen) Huang, Richard Miller, John R. Wagner
US Army TARDEC-Denise Rizzo, Katherine Sebeck, Scott Shurin
  • Journal Article
  • 2018-01-1111
Published 2018-04-03 by SAE International in United States
Improved propulsion system cooling remains an important challenge in the transportation industry as heat generating components, embedded in ground vehicles, trend toward higher heat fluxes and power requirements. The further minimization of the thermal management system power consumption necessitates the integration of parallel heat rejection strategies to maintain prescribed temperature limits. When properly designed, the cooling solution will offer lower noise, weight, and total volume while improving system durability, reliability, and power efficiency. This study investigates the integration of high thermal conductivity (HTC) materials, carbon fibers, and heat pipes with conventional liquid cooling to create a hybrid “thermal bus” to move the thermal energy from the heat source(s) to the ambient surroundings. The innovative design can transfer heat between the separated heat source(s) and heat sink(s) without sensitivity to gravity. A case study examines the thermal stability, heat dissipation capabilities, power requirements, and system weights for several driving cycles. Representative numerical results show that the HTC materials and carbon fibers offer moderate cooling while loop heat pipes provide significant improvements for passive cooling.
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An Integrated Cooling System for Hybrid Electric Vehicle Motors: Design and Simulation

SAE International Journal of Commercial Vehicles

Clemson University-Junkui (Allen) Huang, Shervin Shoai Naini, Richard miller, John R. Wagner
US Army TARDEC-Denise Rizzo, Katherine Sebeck, Scott Shurin
  • Journal Article
  • 2018-01-1108
Published 2018-04-03 by SAE International in United States
Hybrid electric vehicles offer the advantages of reduced emissions and greater travel range in comparison to conventional and electric ground vehicles. Regardless of propulsion strategy, efficient cooling of electric motors remains an open challenge due to the operating cycles and ambient conditions. The onboard thermal management system must remove the generated heat so that the motors and other vehicle components operate within their designed temperature ranges. In this article, an integrated thermal structure, or cradle, is designed to efficiently transfer heat within the motor housing to the end plates for transmission to an external heat exchanger. A radial array of heat pipes function as an efficient thermal connector between the motor and heat connector, or thermal bus, depending on the configuration. Cooling performance has been evaluated for various driving cycles. Numerical results show that 1.3 kW of peak heat wattage can be accommodated with free convection while 3.2 kW is obtained by adding forced convection using 13.7 W of electric power. The internal motor temperature is maintained within the prescribed limits of 75°C and 55°C…
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Fuel Consumption Reduction on Heavy-Duty and Light-Duty Commercial Vehicles by Means of Advanced Central Tire Inflation Systems

SAE International Journal of Commercial Vehicles

CNH Industrial-Ivan Calaon, Enrica Capitelli, Vladi Nosenzo, Alessio Sarcoli
Politecnico di Torino-Stefano D'Ambrosio, Elia Francesco Mameli, Roberto Vitolo
  • Journal Article
  • 2018-01-1334
Published 2018-04-03 by SAE International in United States
Tire inflation pressure has a relevant impact on fuel consumption and tire wear, and therefore affects both CO2 emissions and the total cost of ownership (TCO). The latter is extremely important in the case of commercial vehicles, where the cost of fuel is responsible for about 30% of the TCO.A possible advanced central tire inflation system, which is able to inflate and deflate tires autonomously, as part of a smart energy management system and as an active safety device, has been studied. This system allows misuse due to underinflation to be avoided and adapts the tires to the current working conditions of the vehicle. For instance, the tire pressure can be adapted according to the carried load or during tire warm-up. An on board software is able to evaluate the working conditions of the vehicle and select the tire pressure that minimizes the energy expense, the TCO, or the braking distance, according to a multi-objective optimization strategy.A simulation tool has been set up to evaluate the effects of tire pressure on fuel consumption and tire…
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A Machine Learning-Genetic Algorithm (ML-GA) Approach for Rapid Optimization Using High-Performance Computing

SAE International Journal of Commercial Vehicles

Aramco Research Center-Yuanjiang Pei, Yu Zhang
Argonne National Laboratory-Ahmed Abdul Moiz, Pinaki Pal, Sibendu Som, Janardhan Kodavasal
  • Journal Article
  • 2018-01-0190
Published 2018-04-03 by SAE International in United States
A Machine Learning-Genetic Algorithm (ML-GA) approach was developed to virtually discover optimum designs using training data generated from multi-dimensional simulations. Machine learning (ML) presents a pathway to transform complex physical processes that occur in a combustion engine into compact informational processes. In the present work, a total of over 2000 sector-mesh computational fluid dynamics (CFD) simulations of a heavy-duty engine were performed. These were run concurrently on a supercomputer to reduce overall turnaround time. The engine being optimized was run on a low-octane (RON70) gasoline fuel under partially premixed compression ignition (PPCI) mode. A total of nine input parameters were varied, and the CFD simulation cases were generated by randomly sampling points from this nine-dimensional input space. These input parameters included fuel injection strategy, injector design, and various in-cylinder flow and thermodynamic conditions at intake valve closure (IVC). The outputs (targets) of interest from these simulations included five metrics related to engine performance and emissions. Over 2000 samples generated from CFD were then used to train an ML model that could predict these five targets…
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