Heavy Duty Truck Fuel Consumption Prediction Based on Driving Cycle Properties

Turbochargers with variable turbine geometry (VGT) are established in diesel engines for passenger cars because of the beneficial effect on transient operation. The variability permits the reduction of exhaust back pressure, resulting in lower fuel consumption. There are only a few applications in heavy duty truck engines due to increased mechanical complexity and vulnerability to failure. This paper presents a turbine concept with a simple variability developed for a heavy duty engine. The variability is achieved upstream of the rotor by changing the sectional area of the volute. This can be done through a rotationally movable ring which shifts the circumferential position of the volute tongues. These separate both scrolls of a double segment turbine and can be rotated by an electric actuator. The performance maps measured at the hot gas test stand show the large variability of the flow parameter and the high efficiency levels over the operating range of the variable asymmetric turbine (VAT). The flow field is computed by the use of 3D-CFD simulations in order to analyze the loss-generating mechanisms that occur within the machine. Test runs on an engine test stand demonstrate the high potential of the concept concerning reduction of fuel consumption and a wide scope of realizable EGR rates in order to reduce NOx emissions in a cost-effective way. The resultant large mass flow variability allows the deletion of the waste gate and enables efficiency improvements.

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The Effect of Low Viscosity Oil on the Wear, Friction and Fuel Consumption of a Heavy Duty Truck Engine

SAE International Journal of Fuels and Lubricants ◽

10.4271/2013-01-0331 ◽

2013 ◽

Vol 6 (2) ◽

pp. 311-319 ◽

Cited By ~ 16

Author(s):

Phil Carden ◽

Carl Pisani ◽

Jon Andersson ◽

Ian Field ◽

Emmanuel Lainé ◽

...

Keyword(s):

Fuel Consumption ◽

Heavy Duty ◽

Heavy Duty Truck ◽

Low Viscosity ◽

Wear Friction ◽

Truck Engine

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Adaptive Equivalent Consumption Minimization Strategy for Hybrid Heavy-Duty Truck Based on Driving Condition Recognition and Parameter Optimization

Energies ◽

10.3390/en13205407 ◽

2020 ◽

Vol 13 (20) ◽

pp. 5407

Author(s):

Pei Zhang ◽

Xianpan Wu ◽

Changqing Du ◽

Hongming Xu ◽

Huawu Wang

Keyword(s):

Real Time ◽

Parameter Optimization ◽

Clustering Algorithm ◽

Accurate Determination ◽

Driving Cycle ◽

Heavy Duty ◽

Heavy Duty Truck ◽

Driving Condition ◽

Equivalent Consumption Minimization Strategy ◽

Condition Recognition

The accurate determination and dynamic adjustment of key control parameters are challenges for equivalent consumption minimization strategy (ECMS) to be implemented in real-time control of hybrid electric vehicles. An adaptive real-time ECMS is proposed for hybrid heavy-duty truck in this paper. Three efforts have been made in this study. First, six kinds of typical driving cycle for hybrid heavy-duty truck are obtained by hierarchical clustering algorithm, and a driving condition recognition (DCR) algorithm based on a neural network is put forward. Second, particle swarm optimization (PSO) is applied to optimize three key parameters of ECMS under a specified driving cycle, including equivalent factor, scale factor of penalty function, and vehicle speed threshold for engine start-up. Finally, combining all the above two efforts, a novel adaptive ECMS based on DCR and key parameter optimization of ECMS by PSO is presented and validated through numerical simulation. The simulation results manifest that proposed adaptive ECMS can further improve the fuel economy of a hybrid heavy-duty truck while keeping the battery charge-sustainability, compared with ECMS and PSO-ECMS under a composite driving cycle.

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