High-Fidelity Modeling of a Power-Split Plug-In Hybrid Electric Powertrain for Control Performance Evaluation

Taghavipour, Amir; Masoudi, Ramin; Azad, Nasser L.; McPhee, John

doi:10.1115/detc2013-13489

Cited by 23 publications

(16 citation statements)

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“…In line with increasing interest in using Autonomie to test the performance of controllers, the authors take the same approach to study the advantages and disadvantages of the proposed controller. It is worth noting that the validity of the developed high-fidelity model has been tested in a previous publication by the author's research group [31].…”

Section: ) High-fidelity Modelmentioning

confidence: 99%

Ecological Adaptive Cruise Controller for Plug-In Hybrid Electric Vehicles Using Nonlinear Model Predictive Control

Vajedi

Azad

2016

IEEE Trans. Intell. Transport. Syst.

164

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Plug-in hybrid electric vehicles (PHEVs) are promising options for future transportation. Having two sources of energy enables them to offer better fuel economy and fewer emissions. Significant research has been done to take advantage of future route information to enhance vehicle performance. In this paper, an ecological adaptive cruise controller (Eco-ACC) is used to improve both fuel economy and safety of the Toyota Prius Plug-in Hybrid. Recently, an emerging trend in the research has been to improve the adaptive cruise controller. However, the majority of research to date has focused on driving safety, and only rare reports in the literature substantiate the applicability of such systems for PHEVs. Here, we demonstrate that using an Eco-ACC system can simultaneously improve total energy costs and vehicle safety. The developed controller is equipped with an onboard sensor that captures upcoming trip data to optimally adjust the speed of PHEVs. The nonlinear model predictive control technique (NMPC) is used to optimally control vehicle speed. To prepare the NMPC controller for real-time applications, a fast and efficient control-oriented model is developed. The authenticity of the model is validated using a high-fidelity Autonomie-based model. To evaluate the designed controller, the global optimum solution for cruise control problem is found using Pontryagin's minimum principle (PMP). To explore the efficacy of the controller, PID and linear MPC controllers are also applied to the same problem. Simulations are conducted for different driving scenarios such as driving over a hill and car following. These simulations demonstrate that NMPC improves the total energy cost up to 19%. Index Terms-Ecological adaptive cruise controller, intelligent transportation systems, advanced driving assistant systems, nonlinear model predictive controller. ManuscriptMahyar Vajedi received the B.Sc. and M.Sc. degrees in mechanical engineering from Sharif University of Technology, Tehran, Iran. He is currently working toward the Ph.D. degree with the Department of Systems Design Engineering, University of Waterloo. He is the author of several published papers. His research interests include intelligent vehicle control systems, such as eco-cruise controllers, adaptive cruise controllers, and intelligent energy management systems for electric propulsion vehicles. Nasser L. Azad is currently an Assistant Professor with the Department of Systems Design Engineering, University of Waterloo. His research program operates at the intersection of advanced vehicle electrification and vehicle communication technologies, where he specializes in the development of intelligent automotive controllers that can leverage information provided by emerging ITS, GPS, GIS, and advanced wireless technologies to optimize key performance characteristics, such as fuel economy and emission reduction, in real time. The multidisciplinary nature of his research has allowed him to establish strong collaborative networks with researchers from universities in Europe...

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Section: ) High-fidelity Modelmentioning

confidence: 99%

Ecological Adaptive Cruise Controller for Plug-In Hybrid Electric Vehicles Using Nonlinear Model Predictive Control

Vajedi

Azad

2016

IEEE Trans. Intell. Transport. Syst.

164

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“…A schematic of the whole simulation model is demonstrated in Figure . This model is cross‐validated with the experimental database available in the Autonomie software, which is developed by Argonne National laboratory …”

Section: Plug‐in Hybrid Electric Vehicles High‐fidelity Model In Maplmentioning

confidence: 99%

“…The driveline connects the motor (ring gear), generator (sun gear), and mean value engine (carrier) via a power‐split device. The ring gear shaft is connected to the final drive with appropriate gear ratios …”

Section: Plug‐in Hybrid Electric Vehicles High‐fidelity Model In Maplmentioning

confidence: 99%

Control‐relevant parameter estimation application to a model‐based PHEV power management system

Taghavipour

Masoudi

Azad

et al. 2017

Optim Control Appl Methods

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Summary Explicit model predictive control approach is a promising approach to fulfill automotive real‐time controls requirements. A key factor in the performance and real‐time capabilities of a predictive model‐based controller is the accuracy of the control‐oriented model. The control‐oriented model should capture the essential dynamics of the real plant and be adequately simple to make the controller implementable on a commercial hardware with limited memory and computational capabilities. In this study, control‐relevant parameter estimation is used to find a control‐oriented model for a real‐time predictive power management system for a plug‐in hybrid powertrain. Simulations, which are conducted using an equation‐based model of the powertrain, demonstrate a significant improvement of the power management system performance by improving the control‐oriented model with no effect on real‐time capabilities of the controller.

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“…The considered baseline vehicle simulation model was developed and validated in the Autonomie environment in a previous study (Taghavipour et al 2013). The detailed characteristics of this model can be found in that reference, and the model's characteristics are not described here for the sake of brevity.…”

Section: Power-split Plug-in Hybrid Electric Vehicle Modellingmentioning

confidence: 99%

“…For this study, a driving cycle from the Environmental Protection Agency, known as the Urban Dynamometer Driving Schedule (UDDS), the Highway Fuel Economy Test (HWFET) and Federal Test Procedure (FTP) driving cycles are considered (Taghavipour et al 2013;Vajedi, Chehresaz, and Azad 2014 …”

Section: Driving Cyclesmentioning

confidence: 99%

Multi-objective component sizing of a power-split plug-in hybrid electric vehicle powertrain using Pareto-based natural optimization machines

Mozaffari

Vajedi

Chehresaz

et al. 2015

Engineering Optimization

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The urgent need to meet increasingly tight environmental regulations and new fuel economy requirements has motivated system science researchers and automotive engineers to take advantage of emerging computational techniques to further advance hybrid electric vehicle and plug-in hybrid electric vehicle (PHEV) designs. In particular, research has focused on vehicle powertrain system design optimization, to reduce the fuel consumption and total energy cost while improving the vehicle's driving performance. In this work, two different natural optimization machines, namely the synchronous self-learning Pareto strategy and the elitism non-dominated sorting genetic algorithm, are implemented for component sizing of a specific power-split PHEV platform with a Toyota plug-in Prius as the baseline vehicle. To do this, a high-fidelity model of the Toyota plug-in Prius is employed for the numerical experiments using the Autonomie simulation software. Based on the simulation results, it is demonstrated that Pareto-based algorithms can successfully optimize the design parameters of the vehicle powertrain.

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High-Fidelity Modeling of a Power-Split Plug-In Hybrid Electric Powertrain for Control Performance Evaluation

Cited by 23 publications

References 0 publications

Ecological Adaptive Cruise Controller for Plug-In Hybrid Electric Vehicles Using Nonlinear Model Predictive Control

Ecological Adaptive Cruise Controller for Plug-In Hybrid Electric Vehicles Using Nonlinear Model Predictive Control

Control‐relevant parameter estimation application to a model‐based PHEV power management system

Multi-objective component sizing of a power-split plug-in hybrid electric vehicle powertrain using Pareto-based natural optimization machines

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