Design optimization and optimal control for hybrid vehicles

Sinoquet, Delphine; Rousseau, Grégory; Milhau, Yohan

doi:10.1007/s11081-009-9100-8

Cited by 76 publications

(42 citation statements)

References 12 publications

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“…The cost function is calculated for the entire route and is minimized [25]. The cost function accounts for the amount of fuel being consumed each time step.…”

Section: Dynamic Programming Optimizationmentioning

confidence: 99%

Look-Ahead Information Based Optimization Strategy for Hybrid Electric Vehicles

Alzorgan¹,

Carroll²,

Al-Masalmeh³

et al. 2016

SAE Technical Paper Series

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The environmental impact of the fossil fuels has increased tremendously in the last decade. This impact is one of the most contributing factors of global warming. This research aims to reduce the amount of fuel consumed by vehicles through optimizing the control scheme for the future route information. Taking advantage of more degrees of freedom available within PHEV, HEV, and FCHEV "energy management" allows more margin to maximize efficiency in the propulsion systems. The application focuses on reducing the energy consumption in vehicles by acquiring information about the road grade. Road elevations are obtained by use of Geographic Information System (GIS) maps to optimize the controller. The optimization is then reflected on the powertrain of the vehicle.The approach uses a Model Predictive Control (MPC) algorithm that allows the energy management strategy to leverage road grade to prepare the vehicle for minimizing energy consumption during an uphill and potential energy harvesting during a downhill.The control algorithm will predict future energy/power requirements of the vehicle and optimize the performance by instructing the power split between the internal combustion engine (ICE) and the electric-drive system. Allowing for more efficient operation and higher performance of the PHEV, and HEV. Implementation of different strategies, such as MPC and Dynamic Programming (DP), is considered for optimizing energy management systems. These strategies are utilized to have a low processing time. This approach allows the optimization to be integrated with ADAS applications, using current technology for implementable real time applications.ii The Thesis presents multiple control strategies designed, implemented, and tested using real-world road elevation data from three different routes. Initial simulation based results show significant energy savings. The savings range between 11.84% and 25.5% for both Rule Based (RB) and DP strategies on the real world tested routes. Future work will take advantage of vehicle connectivity and ADAS systems to utilize Vehicle to Vehicle (V2V), Vehicle to Infrastructure (V2I), traffic information, and sensor fusion to further optimize the PHEV and HEV toward more energy efficient operation.iii ACKNOWLEDGMENTS I would first wish to thank my advisor Dr. Abdel Ra'ouf Mayyas of the Ira A. Fulton Schools of Engineering at Arizona State University, for his support and guidance throughout the work on this research. He hired me to be a Graduate Research Assistance on the ASU EcoCAR3 team. His support was treasured, especially when facing difficulties working on the project. I want to thank the committee members for the experience they provided me through meetings and classes, which was essential in conducting this research.

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“…The cost function is calculated for the entire route and is minimized [25]. The cost function accounts for the amount of fuel being consumed each time step.…”

Section: Dynamic Programming Optimizationmentioning

confidence: 99%

Look-Ahead Information Based Optimization Strategy for Hybrid Electric Vehicles

Alzorgan¹,

Carroll²,

Al-Masalmeh³

et al. 2016

SAE Technical Paper Series

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“…The system dynamics are described by the battery state of energy (6) and the transmission gear position (9). The corresponding control variables, battery power and gear command, are continuous and discrete respectively.…”

Section: A Optimal Control Problem Formulationmentioning

confidence: 99%

Predictive gear shift control for a parallel Hybrid Electric Vehicle

Ngo

Hofman

Steinbuch

et al. 2011

2011 IEEE Vehicle Power and Propulsion Conference

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In this paper, Model Predictive Control (MPC) framework is exploited to synthesize a predictive controller for a parallel Hybrid Electric Vehicle (HEV) equipped with an Automated Manual Transmission. The algorithm also controls the gear shift command, together with the power split between the engine and electric machine and the engine on-off state using the route information ahead. A non-predictive controller based on a combination of Dynamic Programming (DP) and Pontryagin's Minimum Principle (PMP) is described and taken as a benchmark control solution for optimizing the gear shift problem of the parallel HEV in terms of computational efficiency. This so-called DP-PMP control approach is then utilized in the MPC framework to realize the predictive controller for a gear shift problem in a receding horizon mode. Simulation results show that the non-predictive controller improves the fuel economy up to 35.9% and 43.5% on NEDC and FTP75 respectively when compared with a conventional vehicle. Even with a short horizon, fuel saving of the predictive controller is very close to that of the non-predictive controller with a relative difference of 0.3%. Moreover, the predictive controller can be seen as a suitable realtime implementable control candidate with a fast computation property.

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“…Thus, the delta SOC plays an important role in the development of the EMS algorithm. Since the delta SOC is a design constraint (requirement), it is then handled as a penalty function (Sinoquet et al 2011).…”

Section: Introductionmentioning

confidence: 99%

Torque control strategy and optimization for fuel consumption and emission reduction in parallel hybrid electric vehicles

Kitayama

Saikyo

Nishio

et al. 2015

Struct Multidisc Optim

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To reduce fuel consumption and exhaust emissions in hybrid electric vehicles (HEVs), it is important to develop a well-organized energy management system (EMS). This paper proposes a torque control strategy coupled with optimization for a parallel HEV. A torque control strategy is developed first. In particular, a function to control the driving condition, called the internal combustion engine (ICE) torque control function, is introduced. This function controls the driving conditions (electric motor (EM) driving, ICE driving, and ICE driving assisted by EM) for reducing fuel consumption and exhaust emissions. This function depends on several design variables that should be optimized. Numerical simulation of HEV using Matlab/Simulink is so computationally intensive that a sequential approximate optimization (SAO) using a radial basis function network (RBF) is adopted to determine the optimal values of these design variables. As the result, the optimal ICE torque control function is determined with a small number of simulation runs. In this paper, CO 2 and NO x emissions are minimized simultaneously for reducing the fuel consumption and exhaust emission. Through numerical simulations using typical driving cycles, the trade-off between CO 2 and NO x emissions is clarified and the validity of the proposed torque control strategy coupled with the proposed optimization is examined.

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Design optimization and optimal control for hybrid vehicles

Cited by 76 publications

References 12 publications

Look-Ahead Information Based Optimization Strategy for Hybrid Electric Vehicles

Look-Ahead Information Based Optimization Strategy for Hybrid Electric Vehicles

Predictive gear shift control for a parallel Hybrid Electric Vehicle

Torque control strategy and optimization for fuel consumption and emission reduction in parallel hybrid electric vehicles

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