On-Road Validation of a Simplified Model for Estimating Real-World Fuel Economy

Wood, Eric; Gonder, Jeffrey; Jehlik, Forrest

doi:10.4271/2017-01-0892

Cited by 7 publications

(3 citation statements)

References 2 publications

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“…Specifically, it summarizes the calibration of FASTSim to an individual vehicle using chassis dynamometer data over standard drive cycles, followed by validation of the model against data collected during on-road operation of the vehicle. See Wood et al (2017) for additional details.…”

Section: On-road/real-world Validationmentioning

confidence: 99%

Future Automotive Systems Technology Simulator (FASTSim) Validation Report

Baker

Moniot

Brooker

et al. 2018

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Section: On-road/real-world Validationmentioning

confidence: 99%

Future Automotive Systems Technology Simulator (FASTSim) Validation Report

Baker

Moniot

Brooker

et al. 2018

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show abstract

“…FASTSim [6] and Autonomie [10] are example frameworks that support the customization of modular physics-based models to different vehicles by modifying the equations, maps, and parameters that define each module. In the specific case of fuel consumption, a physics-based model for a conventional gasoline sedan is described in [11]. Since physics-based models rely on a deep understanding of the laws of physics that govern the behavior of the system, these models can be highly accurate [5,7].…”

Section: Introductionmentioning

confidence: 99%

Route-Sensitive Fuel Consumption Models for Heavy-Duty Vehicles

Schoen¹,

Byerly²,

Santos³

et al. 2020

SAE Int. J. Commer. Veh.

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This article investigates the ability of data-driven models to estimate instantaneous fuel consumption over 1 km road segments from different routes for different heavy-duty vehicles from the same fleet. Models are created using three different techniques: parametric, linear regression, and artificial neural networks. The proposed models use features derived from vehicle speed, mass, and road grade, which can be easily obtained from telematics devices, in addition to power take-off (PTO) active time, which is needed to capture the power requested by accessories in several heavy-duty vehicles. The robustness of these models with respect to the training data selection is improved by using k-fold cross-validation. Moreover, the inherent underestimation or overestimation bias of the model is calculated and used to offset the fuel consumption estimates for new routes. The study shows that the target application dictates the choice of model features. In fact, the results indicate that depending on the vocation the linear regression and neural network models, which use the same input features, are able to adequately differentiate between the fuel consumption of two vehicles from the same fleet as well as between the fuel consumption of a single vehicle over two different routes. However, the parametric model, which does not utilize PTO active time, is unable to differentiate between two vehicles from the same fleet. This latter model is more suitable for comparing fuel consumption across different fleets of vehicles. In summary, vocation-specific models should be used to optimize fuel consumption for a given fleet of vehicles, whereas general models can only provide insight into aggregated fuel consumption for entire fleets. Moreover, both the accuracy and the precision of the models as measured by their confidence interval should be taken into consideration when comparing fuel consumption estimates for two vehicles from the same fleet or the fuel consumption estimates of an individual vehicle for two different routes. This study shows that the artificial neural network models have narrow 95% confidence intervals and are therefore more precise than the equivalent linear regression models.

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“…Previous efforts in understanding real-world effects on vehicle efficiency focused on the vehicle system thermal effects [10] . This work presents a methodology to determine the off-cycle fuel economy benefit of a 2-Layer HVAC [11] system versus a vehicle without the technology.…”

mentioning

confidence: 99%

Determining Off-cycle Fuel Economy Benefits of 2-Layer HVAC Technology

Jehlik

Chevers

Moniot

et al. 2018

SAE Technical Paper Series

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his work presents a methodology to determine the off-cycle fuel economy benefit of a 2-Layer HVAC system which reduces ventilation and heat rejection losses of the heater core versus a vehicle using a standard system. Experimental dynamometer tests using EPA drive cycles over a broad range of ambient temperatures were conducted on a highly instrumented 2016 Lexus RX350 (3.5L, 8 speed automatic). These tests were conducted to measure differences in engine efficiency caused by changes in engine warmup due to the 2-Layer HVAC technology in use versus the technology being disabled (disabled equals fresh air-considered as the standard technology baseline). These experimental datasets were used to develop simplified response surface and lumped capacitance vehicle thermal models predictive of vehicle efficiency as a function of thermal state. These vehicle models were integrated into a database of measured on road testing and coupled with U.S. typical meteorological data to simulate vehicle efficiency across seasonal thermal and operational conditions for hundreds of thousands of drive cycles. Fuel economy benefits utilizing the 2-Layer HVAC technology are presented in addition to goodness of fit statistics of the modeling approach relative to the experimental test data. FIGURE 1 2-Layer air flows © SAE International FIGURE 2 2016 Lexus RX350 test vehicle on APRF dynamometer for testing.

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On-Road Validation of a Simplified Model for Estimating Real-World Fuel Economy

Cited by 7 publications

References 2 publications

Future Automotive Systems Technology Simulator (FASTSim) Validation Report

Future Automotive Systems Technology Simulator (FASTSim) Validation Report

Route-Sensitive Fuel Consumption Models for Heavy-Duty Vehicles

Determining Off-cycle Fuel Economy Benefits of 2-Layer HVAC Technology

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