Autonomie Model Validation with Test Data for 2010 Toyota Prius

Kim, Nam Wook; Rousseau, Aymeric; Rask, Eric

doi:10.4271/2012-01-1040

Cited by 79 publications

(43 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The model used to represent the baseline EMS is consistent with previous research in that it is a modification of the publically available 2004 Toyota Prius in the Autonomie modeling software to represent a 2010 Toyota Prius [25]. The Autonomie modeling software has demonstrated strong correlation with real-world testing and is generally accepted as the standard among industry and research professionals [31].…”

Section: Baseline Energy Management Strategy Simulationsupporting

confidence: 61%

Towards Improving Vehicle Fuel Economy with ADAS

Tunnell

Asher

Pasricha

et al. 2018

SAE Technical Paper Series

View full text Add to dashboard Cite

Modern vehicles have incorporated numerous safety-focused advanced driver-assistance systems (ADAS) in the last decade including smart cruise control and object avoidance. In this article, we aim to go beyond using ADAS for safety and propose to use ADAS technology to enable predictive optimal energy management and improve vehicle fuel economy (FE). We combine ADAS sensor data with a previously developed prediction model, dynamic programming (DP) optimal energy management control, and a validated model of a 2010 Toyota Prius to explore FE. First, a unique ADAS detection scope is defined based on optimal vehicle control prediction aspects demonstrated to be relevant from the literature. Next, during real-world city and highway drive cycles in Denver, Colorado, a camera is used to record video footage of the vehicle environment and define ADAS detection ground truth. Then, various ADAS algorithms are combined, modified, and compared to the ground truth results. Lastly, the impact of four vehicle control strategies on FE is evaluated: (1) the existing vehicle control, (2) actual ADAS detection for prediction and optimal energy management (we consider two variants ADAS1 and ADAS2 for this strategy), (3) ground truth ADAS detection for prediction and optimal energy management, and (4) 100% accurate prediction and optimal energy management. Results show that the defined ADAS scope and algorithms provide close correlation with ADAS ground truth and can enable FE improvements as part of a prediction-based optimal energy management strategy (EMS). Our proposed approach can leverage existing ADAS technology in modern vehicles to realize prediction-based optimal energy management, thus obtaining FE improvements with minor modifications.

show abstract

Section: Baseline Energy Management Strategy Simulationsupporting

confidence: 61%

Towards Improving Vehicle Fuel Economy with ADAS

Tunnell

Asher

Pasricha

et al. 2018

SAE Technical Paper Series

View full text Add to dashboard Cite

show abstract

“…Also, the fuel economy is increased, and a detail comparison analysis based on testing data shows that the system efficiency of MY10 is improved compared with MY04. 10,11 Filtering the test data Figure 4 shows a general organization of controllers. Although noise signals are already filtered by sophisticated data-acquisition processes at the test bench, the obtained test data are not supposed to be directly used for the control analysis, since the obtained data are frequently output signals of a control unit or system feedbacks, as shown in Figure 4.…”

Section: Prius My10mentioning

confidence: 99%

Vehicle-level control analysis of 2010 Toyota Prius based on test data

Kim

Rousseau

Rask

2012

Proceedings of the Institution of Mechanical Engineers, Part D:

Self Cite

View full text Add to dashboard Cite

The Prius, a power-split hybrid electric vehicle developed by Toyota, has been the top-selling vehicle in the United States hybrid electric vehicle market for the last decade. The transmission system of the vehicle is a frequent theme of study for hybrid electric vehicles. However, the control concept of the vehicle is not well known, since analyzing control behaviors requires well-designed facilities to obtain testing results and well-defined processes to analyze the obtained results. Argonne National Laboratory has these resources and capabilities. In addition, Argonne has produced a reliable simulation tool, Autonomie, by which a vehicle model for the 2010 Prius is developed on the basis of the analyzed results, and it is validated with the results of testing. The developed model demonstrates that results of vehicle performance from simulation are close to those of from real-world tests-within 5%. The main focus of this study is to provide information about the supervisory control for the 2010 Prius, so that researchers can reproduce the real-world behavior of the vehicle through simulations. The analyzed control ideas based on the testing results will be very helpful in terms of understanding the control behavior of the vehicle, and the information resulting from this study is useful to develop the controller for the vehicle at a simulation level.

show abstract

“…Te negative portion of vehicle tractive power (P tractive ) multiplied by the regenerative braking energy efciency and the positive friction and engine inertia torque losses (T loss ) were added to estimate the electric power applied to the BISG motor for P0 applications. Te regenerative brake power, P rgen_brake , [11,12] of the BISG is calculated by using 48 V motor/generator efciency maps as shown in Eq. (4):…”

Section: Power-coupling and Transmission Te P0 And P2mentioning

confidence: 99%

Modeling and Controls Development of 48 V Mild Hybrid Electric Vehicles

Lee¹,

Cherry²,

Safoutin³

et al. 2018

SAE Technical Paper Series

View full text Add to dashboard Cite

T he Advanced Light-Duty Powertrain and Hybrid Analysis tool (ALPHA) was created by EPA to evaluate the Greenhouse Gas (GHG) emissions of Light-Duty (LD) vehicles. ALPHA is a physics-based, forward-looking, full vehicle computer simulator capable of analyzing various vehicle types combined with diferent powertrain technologies. Te ALPHA desktop application was developed using MATLAB/Simulink. Te ALPHA tool was used to evaluate technology efectiveness and of-cycle technologies such as air-conditioning, electrical load reduction technology and road load reduction technologies of conventional, non-hybrid vehicles for the Midterm Evaluation of the 2017-2025 LD GHG rule by the U.S. Environmental Protection Agency (EPA) Ofce of Transportation and Air Quality (OTAQ). Tis paper presents controls development, modeling results, and model validation for simulations of a vehicle with a 48 V Belt Integrated Starter Generator (BISG) mild hybrid electric vehicle and an initial model design for a 48 V inline on-axis P2-confguration mild hybrid electric vehicle. Both confgurations were modeled with a MATLAB/Simulink/Statefow tool, which has been integrated into EPA's ALPHA vehicle model and was also used to model components within Gamma Technology GT-DRIVE simulations. Te mild hybrid electric vehicle model was validated using vehicle data obtained from Argonne National Laboratory (ANL) chassis dynamometer tests of a 2013 Chevrolet Malibu Eco 115 V 15 kW BISG mild hybrid electric vehicle. Te simulated fuel economy, engine torque/speed, motor torque/speed, engine on-of controls, battery voltage, current, and State of Charge (SOC) were all in good agreement with the vehicle test data on a number of drive schedules. Te developed 48 V mild hybrid electric vehicle model can be used to estimate the GHG emissions and fuel economy of 48 V mild hybrid electric vehicles over the EPA regulatory drive cycles and to estimate of-cycle GHG emissions. Te 48 V mild hybrid electric vehicle model will be further validated with additional 48 V mild hybrid electric vehicle test data in the future as more vehicle models become available. EPA has included 48 V BISG mild hybrid electric vehicle technology in its assessment of CO2-reducing technologies available for compliance with U.S. GHG standards.

show abstract

Autonomie Model Validation with Test Data for 2010 Toyota Prius

Cited by 79 publications

References 7 publications

Towards Improving Vehicle Fuel Economy with ADAS

Towards Improving Vehicle Fuel Economy with ADAS

Vehicle-level control analysis of 2010 Toyota Prius based on test data

Modeling and Controls Development of 48 V Mild Hybrid Electric Vehicles

Contact Info

Product

Resources

About