Characterizing Factors Influencing SI Engine Transient Fuel Consumption for Vehicle Simulation in ALPHA

Dekraker, Paul; Stuhldreher, Mark; Kim, Youngki

doi:10.4271/2017-01-0533

Cited by 10 publications

(5 citation statements)

References 11 publications

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“…The adoption of a static map typically leads to an estimate of the fuel consumption that is usually lower than what is measured in dynamic conditions by means of a chassis dynamometer [36]. However, this difference is in the range of 1-4% depending on the driving cycle [36], with more aggressive driving cycles being associated with the upper bound of this range. In this paper, the extra fuel consumption due to transient operation is neglected.…”

Section: Internal Combustion Enginementioning

confidence: 99%

“…The fuel consumption map of Mazda CX9 2016 ICE used in the simulation is shown in Figure 4 as obtained from experimental data [32]. The adoption of a static map typically leads to an estimate of the fuel consumption that is usually lower than what is measured in dynamic conditions by means of a chassis dynamometer [36]. However, this difference is in the range of 1-4% depending on the driving cycle [36], with more aggressive driving cycles being associated with the upper bound of this range.…”

Section: Internal Combustion Enginementioning

confidence: 99%

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Battery Sizing for Mild P2 HEVs Considering the Battery Pack Thermal Limitations

et al. 2021

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Small capacity and passively cooled battery packs are widely used in mild hybrid electric vehicles (MHEV). In this regard, continuous usage of electric traction could cause thermal runaway of the battery, reducing its life and increasing the risk of fire incidence. Hence, thermal limitations on the battery could be implemented in a supervisory controller to avoid such risks. A vast literature on the topic shows that the problem of battery thermal runaway is solved by applying active cooling or by implementing penalty factors on electric energy utilization for large capacity battery packs. However, they do not address the problem in the case of passive cooled, small capacity battery packs. In this paper, an experimentally validated electro-thermal model of the battery pack is integrated with the hybrid electric vehicle simulator. A supervisory controller using the equivalent consumption minimization strategy with, and without, consideration of thermal limitations are discussed. The results of a simulation of an MHEV with a 0.9 kWh battery pack showed that the thermal limitations of the battery pack caused a 2–3% fuel consumption increase compared to the case without such limitations; however, the limitations led to battery temperatures as high as 180 °C. The same simulation showed that the adoption of a 1.8 kWh battery pack led to a fuel consumption reduction of 8–13% without thermal implications.

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Section: Internal Combustion Enginementioning

confidence: 99%

Section: Internal Combustion Enginementioning

confidence: 99%

Battery Sizing for Mild P2 HEVs Considering the Battery Pack Thermal Limitations

et al. 2021

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“…This model has shown to be robust over a range of very different drive cycles and has physics based models for different components, thus leading to a high confidence in the resulting fuel economy numbers. In published validation for different standard EPA drive cycles, the maximum reported error in fuel economy was 2% against experiments [25].…”

Section: Full Vehicle Simulationmentioning

confidence: 99%

A Comparative Study of Different Objectives Functions for the Minimal Fuel Drive Cycle Optimization in Autonomous Vehicles

Prakash

Kim

Stefaopoulou

2019

Journal of Dynamic Systems, Measurement, and Control

Self Cite

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With the advent of self-driving autonomous vehicles, vehicle controllers are free to drive their own velocities. This feature can be exploited to drive an optimal velocity trajectory that minimizes fuel consumption. Two typical approaches to drive cycle optimization are velocity smoothing and tractive energy minimization. The former reduces accelerations and decelerations, and hence, it does not require information of vehicle parameters and resistance forces. On the other hand, the latter reduces tractive energy demand at the wheels of a vehicle. In this work, utilizing an experimentally validated full vehicle simulation software, we show that for conventional gasoline vehicles the lower energy velocity trajectory can consume as much fuel as the velocity smoothing case. This implies that the easily implementable, vehicle agnostic velocity smoothing optimization can be used for velocity optimization rather than the nonlinear tractive energy minimization, which results in a pulse-and-glide trajectory.

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“…Moreover, by incorporating the engine transient behaviour in the controller, the reduction in its transient operation was achieved, resulting in more stable operation. This transient behaviour of the engine is influenced by a combination of factors, and it is difficult to isolate each effect and establish accurate analytical relationships [25].…”

Section: Introductionmentioning

confidence: 99%

“…The works that consider the transient operation of the engine [23,25,27,28] mainly apply transient correction factors to quasi-static fuel consumption [23,27] determined by extensive experimental data [25].…”

Section: Introductionmentioning

confidence: 99%

Impact of Engine Inertia on P2 Mild HEV Fuel Consumption

Yakhshilikova,

Ruzimov,

Tonoli

et al. 2024

WEVJ

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The energy management system (EMS) of a hybrid electric vehicle (HEV) is an algorithm that determines the power split between the electrical and thermal paths. It defines the operating state of the power sources, i.e., the electric motor (EM) and the internal combustion engine (ICE). It is therefore one of the main factors that can significantly influence the fuel consumption and performance of hybrid vehicles. In the transmission path, the power generated by the ICE is in part employed to accelerate the rotating components of the powertrain, such as the crankshaft, flywheel, gears, and shafts. The main inertial components are the crankshaft and the flywheel. This additional power is significant during high-intensity acceleration. Therefore, the actual engine operation is different from that required by the power split unit. This study focuses on exploring the influence of engine inertia on HEV fuel consumption by developing a controller based on an equivalent consumption minimisation strategy (ECMS) that considers crankshaft and flywheel inertia. The optimal solution obtained by the ECMS controller is refined by incorporating the inertia effect of the main rotating components of the engine into the cost function. This reduces the engine operation during high inertial torque transient phases, resulting in a decrease in vehicle CO2 emissions by 2.34, 2.22, and 1.13 g/km for the UDDS, US06, and WLTC driving cycles, respectively.

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Characterizing Factors Influencing SI Engine Transient Fuel Consumption for Vehicle Simulation in ALPHA

Cited by 10 publications

References 11 publications

Battery Sizing for Mild P2 HEVs Considering the Battery Pack Thermal Limitations

Battery Sizing for Mild P2 HEVs Considering the Battery Pack Thermal Limitations

A Comparative Study of Different Objectives Functions for the Minimal Fuel Drive Cycle Optimization in Autonomous Vehicles

Impact of Engine Inertia on P2 Mild HEV Fuel Consumption

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