Scenario MPC for fuel economy optimization of hybrid electric powertrains on real-world driving cycles

2017 ◽  
Author(s):  
Martina Josevski ◽  
Alexander Katriniok ◽  
Dirk Abel
Keyword(s):  
Fuel Economy ◽  
Real World ◽  
Driving Cycles ◽  
Hybrid Electric ◽  
Electric Powertrains

Control and Design Optimization of a Novel Hybrid Electric Powertrain System

2017 ◽  
Author(s):  
Chenyu Yi ◽  
Bogdan Epureanu
Keyword(s):  
Optimal Control ◽  
Design Optimization ◽  
Fuel Economy ◽  
Optimization Problems ◽  
Computational Cost ◽  
Electric Machines ◽  
Hybrid Electric ◽  
Hybrid Electric Powertrain ◽  
And Control ◽  
Electric Powertrains

Control and design optimization of hybrid electric powertrains is necessary to maximize the benefits of novel architectures. Previous studies have proposed multiple optimal and near-optimal control methods, approaches for design optimization, and ways to solve coupled design and control optimization problems for hybrid electric powertrains. This study presents control and design optimization of a novel hybrid electric powertrain architecture to evaluate its performance and potential using physics-based models for the electric machines, the battery and a near-optimal control, namely the equivalent consumption minimization strategy. Design optimization in this paper refers to optimizing the sizes of the powertrain components, i.e. electric machines, battery and final drive. The control and design optimization problem is formulated using nested approach with sequential quadratic programming as design optimization method. Metamodeling is applied to abstract the near-optimal powertrain control model to reduce the computational cost. Fuel economy, sizes of components, and consistency of city and highway fuel economy are reported to evaluate the performance of the powertrain designs. The results suggest an optimal powertrain design and control that grants good performance. The optimal design is shown to be robust and non-sensitive to slight component size changes when evaluated for the near-optimal control.

A framework for development of real-world motorcycle driving cycle in India

2020 ◽  
pp. 095440702097753
Author(s):  
Masilamani Sithananthan ◽  
Ravindra Kumar
Keyword(s):  
Fuel Economy ◽  
Real World ◽  
State Of The Art ◽  
Emission Factors ◽  
Driving Cycle ◽  
Driving Cycles ◽  
Traffic Scenario

This paper proposed a framework for development of real-world driving cycle in India after a thorough review and comparison of motorcycle driving cycles used in different countries. A limited state-of-the art work for the development of driving cycles for motorcycles is available. The motorcycle driving cycles developed by different countries differ from each other in terms of their driving cycle characteristics, emission factors, and fuel economy. This paper reviewed the parameters of real-world driving cycles of motorcycles and compares the same with legislative cycles concerning their characteristics and emissions. The parameters of real-world driving cycles and Indian legislative cycle (IDC) deviate significantly from other legislative cycles in the range of −97% to +1172% and −74% to 284% respectively. The emission factors of the legislative cycle do not match with the realistic emissions measured by real-world driving cycles. This is due to the reason that the legislative cycles do not represent the current traffic scenario and hence need to be revised. A framework is proposed to develop a real-world driving cycle in India.

Impact of Driving Cycles on Greenhouse Gas Emissions and Fuel Economy for SI Car Real World Driving

2008 ◽  
Vol 1 (1) ◽  
pp. 1320-1333 ◽  
Author(s):  
Hu Li ◽  
Gordon E Andrews ◽  
Dimitrios Savvidis ◽  
Basil Daham ◽  
Karl Ropkins ◽  
...  
Keyword(s):  
Greenhouse Gas Emissions ◽  
Greenhouse Gas ◽  
Fuel Economy ◽  
Real World ◽  
Gas Emissions ◽  
Driving Cycles

scholarly journals Comparisons of Real-World Vehicle Energy Efficiency with Dynamometer-Based Ratings and Simulation Models

2021 ◽  
Vol 12 (4) ◽  
pp. 161
Author(s):  
Karim Hamza ◽  
Kang-Ching Chu ◽  
Matthew Favetti ◽  
Peter Keene Benoliel ◽  
Vaishnavi Karanam ◽  
...  
Keyword(s):  
Energy Efficiency ◽  
Electric Vehicles ◽  
Fuel Economy ◽  
Real World ◽  
Hybrid Electric Vehicle ◽  
Energy Intensity ◽  
Hybrid Electric Vehicles ◽  
Combustion Engine ◽  
Simulation Models ◽  
Hybrid Electric

Software tools for fuel economy simulations play an important role during design stages of advanced powertrains. However, calibration of vehicle models versus real-world driving data faces challenges owing to inherent variations in vehicle energy efficiency across different driving conditions and different vehicle owners. This work utilizes datasets of vehicles equipped with OBD/GPS loggers to validate and calibrate FASTSim (software originally developed by NREL) vehicle models. The results show that window-sticker ratings (derived from dynamometer tests) can be reasonably accurate when averaged across many trips by different vehicle owners, but successfully calibrated FASTSim models can have better fidelity. The results in this paper are shown for nine vehicle models, including the following: three battery-electric vehicles (BEVs), four plug-in hybrid electric vehicles (PHEVs), one hybrid electric vehicle (HEV), and one conventional internal combustion engine (CICE) vehicle. The calibrated vehicle models are able to successfully predict the average trip energy intensity within ±3% for an aggregate of trips across multiple vehicle owners, as opposed to within ±10% via window-sticker ratings or baseline FASTSim.

Study the Impact of Driver’s Behavior on the Energy Efficiency of Hybrid Electric Vehicles

2013 ◽  
Author(s):  
Lei Feng ◽  
Bo Chen
Keyword(s):  
Energy Loss ◽  
Electric Vehicle ◽  
Simulation Study ◽  
Fuel Economy ◽  
Hybrid Electric Vehicle ◽  
Fuel Efficiency ◽  
Aggressive Driving ◽  
Driving Cycles ◽  
Hybrid Electric ◽  
The Impact

This paper investigates the impact of driver’s behavior on the fuel efficiency of a hybrid electric vehicle (HEV) and its powertrain components, including engine, motor, and battery. The simulation study focuses on the investigation of power request, power output, energy loss, and operating region of powertrain components with the change of driver’s behavior. It is well known that a noticeable difference between the sticker number fuel economy and actual fuel economy will happen when a driver drives aggressively. To simulate aggressive driving, the input driving cycles are scaled from the baseline driving cycles to increase the level of acceleration/deceleration. With scaled aggressive driving cycles, the simulation result shows a significant change of HEV equivalent fuel economy. In addition, the high power demands of aggressive driving cause engine to operate within a higher fuel rate region. Furthermore, the engine is started and shut down frequently due to the large instantaneous power request peaks, which result in high energy loss. The simulation study of the impact of aggressive driving on the HEV fuel efficiency is conducted for a power-split hybrid electric vehicle using powertrain simulation and analysis software Autonomie developed by Argonne National Laboratory. The performance of the major powertrain components is analyzed when the HEV operates at different level of aggressiveness. The simulation results provide useful information to identify the major factors that need to be included in the vehicle control design to improve the fuel efficiency of HEVs under aggressive driving.

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