scholarly journals Understanding Real-World Variability of Hybrid Electric Vehicle Fuel Economy

2020 ◽  
Vol 9 (2) ◽  
pp. 169-184
Author(s):  
Andrew McGordon ◽  
◽  
Paul Jennings ◽  
Keyword(s):  
Fuel Economy ◽  
Real World ◽  
Hybrid Electric Vehicle ◽  
Hybrid Electric Vehicles ◽  
Combustion Engine ◽  
The Real ◽  
Hybrid Electric ◽  
Time Of Operation ◽  
Base Vehicle ◽  
Toyota Prius

The variability of fuel economy (FE) is of significant importance as that of average FE to realize FE benefits of hybrid electric vehicles (HEVs) consistently by all users in the real world. Over the years, majority of the research has been focused on improving average FE overlooking the variability. Although in recent years few studies have been focused on the reduction of FE variability, no study has been concentrated to understand why certain design has lower FE variability as that of others. This article provides a detailed analysis to decipher the reasons for the FE variability in the real world. This study considered the optimum designs based on two established design optimization methodologies considering Toyota Prius non-plug-in hybrid as a base vehicle. This study analyses the impacts of the parameters of driving patterns and the operation of powertrains on FE variability. The study explains that comparatively bigger internal combustion engine (ICE) in combination with the optimum sizes of generator motor and battery could lead to lower FE variability in the real world due to lesser time of operation of ICE to charge the battery.

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.

scholarly journals Fuel Economy of Plug-In Hybrid Electric and Hybrid Electric Vehicles: Effects of Vehicle Weight, Hybridization Ratio and Ambient Temperature

2020 ◽  
Vol 11 (2) ◽  
pp. 31 ◽  
Author(s):  
Heejung Jung
Keyword(s):  
Ambient Temperature ◽  
Electric Vehicles ◽  
Fuel Economy ◽  
Environmental Protection Agency ◽  
Hybrid Electric Vehicles ◽  
Combustion Engine ◽  
Ambient Temperatures ◽  
Vehicle Weight ◽  
Hybrid Electric ◽  
Hybridization Ratio

Hybrid electric vehicles (HEVs) and plug-in hybrid electric vehicles (PHEVs) are evolving rapidly since the introduction of Toyota Prius into the market in 1997. As the world needs more fuel-efficient vehicles to mitigate climate change, the role of HEVs and PHEVs are becoming ever more important. While fuel economies of HEVs and PHEVs are superior to those of internal combustion engine (ICE) powered vehicles, they are partially powered by batteries and therefore they resemble characteristics of battery electric vehicles (BEVs) such as dependence of fuel economy on ambient temperatures. It is also important to understand how different extent of hybridization (a.k.a., hybridization ratio) affects fuel economy under various driving conditions. In addition, it is of interest to understand how HEVs and PHEVs compare with BEVs at a similar vehicle weight. This study investigated the relationship between vehicle mass and vehicle performance parameters, mainly fuel economy and driving range of PHEVs focused on 2018 and 2019 model years using the test data available from fuel economy website of the US Environmental Protection Agency (EPA). Previous studies relied on modeling to understand mass impact on fuel economy for HEV as there were not enough number of HEVs in the market to draw a trendline at the time. The study also investigated the effect of ambient temperature for HEVs and PHEVs and kinetic energy recovery of the regenerative braking using the vehicle testing data for model year 2013 and 2015 from Idaho National Lab (INL). The current study assesses current state-of-art for PHEVs. It also provides analysis of experimental results for validation of vehicle dynamic and other models for PHEVs and HEVs.

scholarly journals Real-Time Co-optimization of Vehicle Route and Speed Using Generic Algorithm for Improved Fuel Economy

2019 ◽  
Vol 141 (03) ◽  
pp. S08-S15
Author(s):  
Guoming G. Zhu ◽  
Chengsheng Miao
Keyword(s):  
Electric Vehicle ◽  
Electric Vehicles ◽  
Fuel Economy ◽  
Autonomous Vehicles ◽  
Hybrid Electric Vehicle ◽  
High Efficiency ◽  
Combustion Engine ◽  
Cruise Control ◽  
Vehicle Performance ◽  
Hybrid Electric

Making future vehicles intelligent with improved fuel economy and satisfactory emissions are the main drivers for current vehicle research and development. The connected and autonomous vehicles still need years or decades to be widely used in practice. However, some advanced technologies have been developed and deployed for the conventional vehicles to improve the vehicle performance and safety, such as adaptive cruise control (ACC), automatic parking, automatic lane keeping, active safety, super cruise, and so on. On the other hand, the vehicle propulsion system technologies, such as clean and high efficiency combustion, hybrid electric vehicle (HEV), and electric vehicle, are continuously advancing to improve fuel economy with satisfactory emissions for traditional internal combustion engine powered and hybrid electric vehicles or to increase cruise range for electric vehicles.

scholarly journals Model Predictive Control for Connected Hybrid Electric Vehicles

2015 ◽  
Vol 2015 ◽  
pp. 1-15 ◽  
Author(s):  
Kaijiang Yu ◽  
Xiaozhuo Xu ◽  
Qing Liang ◽  
Zhiguo Hu ◽  
Junqi Yang ◽  
...  
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Electric Vehicles ◽  
Fuel Economy ◽  
Hybrid Electric Vehicle ◽  
Hybrid Electric Vehicles ◽  
Control Method ◽  
Fuel Efficiency ◽  
Computation Method ◽  
Hybrid Electric

This paper presents a new model predictive control system for connected hybrid electric vehicles to improve fuel economy. The new features of this study are as follows. First, the battery charge and discharge profile and the driving velocity profile are simultaneously optimized. One is energy management for HEV forPbatt; the other is for the energy consumption minimizing problem of acc control of two vehicles. Second, a system for connected hybrid electric vehicles has been developed considering varying drag coefficients and the road gradients. Third, the fuel model of a typical hybrid electric vehicle is developed using the maps of the engine efficiency characteristics. Fourth, simulations and analysis (under different parameters, i.e., road conditions, vehicle state of charge, etc.) are conducted to verify the effectiveness of the method to achieve higher fuel efficiency. The model predictive control problem is solved using numerical computation method: continuation and generalized minimum residual method. Computer simulation results reveal improvements in fuel economy using the proposed control method.

Optimal Selection of Equivalence Factors for ECMS in Mild Hybrid Electric Vehicles

2021 ◽  
Author(s):  
Shailesh Hegde ◽  
Angelo Bonfitto ◽  
Hadi Rahmeh ◽  
Nicola Amati ◽  
Andrea Tonoli
Keyword(s):  
Electric Vehicles ◽  
Fuel Economy ◽  
Hybrid Electric Vehicle ◽  
Shooting Method ◽  
Hybrid Electric Vehicles ◽  
Optimal Solution ◽  
Pollutant Emissions ◽  
Hybrid Electric ◽  
Mild Hybrid ◽  
Selection Of

Abstract The increasing stringent emissions regulation over the years have shifted the focus of automotive industry towards more efficient fuel economy solutions. One such solution is Hybrid electric architecture, which is able to improve the fuel economy and consequently cutting down emissions. A well known control strategy to solve optimization problem for energy management of Hybrid electric vehicles is ECMS (Equivalent Consumption Minimization Strategy). Finding the best control parameters (equivalence factors) of this strategy may become quite involved. This paper proposes a method for the selection of the optimal equivalence factors, for charging and discharging, by applying genetic algorithm in the case of a P0 mild hybrid electric vehicle. This method is a systematic and deterministic way to guarantee an optimal solution with respect to the trial and error method. The proposed ECMS is compared to a technique available in literature, known as the shooting method, which relies only on one equivalence factor for discharging. It is demonstrated that the performance in terms of pollutant emissions are comparable. However, ECMS with GA always guarantees an optimal solution even in the case of heavy accessory load, when shooting method is not valid anymore, as it does not guarantee a charge sustaining condition.

Architecture Selection for Hybrid Electric Mobility As a Service Vehicle

2020 ◽  
Author(s):  
Noah Schaich ◽  
Christian Free ◽  
Nishan Nekoo ◽  
Michael J. Leamy
Keyword(s):  
Fuel Economy ◽  
Environmental Protection Agency ◽  
Hybrid Electric Vehicle ◽  
Combustion Engine ◽  
Hybrid Vehicle ◽  
Electric Machines ◽  
Georgia Tech ◽  
The Road ◽  
Simulink Model ◽  
Hybrid Electric

Abstract This paper documents the vehicle modeling and fuel economy simulation efforts for the Georgia Tech EcoCAR Mobility Challenge team during Year 1 of the current Advanced Vehicle Technology Competition (AVTC). The goal of the first year of the competition was to propose two possible hybrid electric vehicle (HEV) architectures based on a 2019 Chevrolet Blazer platform that could be built and refined throughout Years 2–4 of the competition. A Simulink vehicle model was used to compare a variety of HEV architectures with several different combinations of engines, batteries, and electric machines. An adaptive Equivalent Consumption Minimization Strategy was used to split torque between the internal combustion engine and two electric machines so that architectures with two electric machines could be modeled. The model was validating by comparing results from simulating a conventional Chevrolet Blazer with the 2.5L naturally aspirated engine to fuel economy results obtained by the Environmental Protection Agency (EPA). In addition to developing a hybrid vehicle, the competition is focused on exploring Mobility as a Service (MaaS), which introduces some special considerations when choosing a hybrid vehicle architecture. The results of the Simulink model as well as requirements set by the MaaS market led the Georgia Tech EcoCAR team to pursue a dual electric machine P0P4 parallel through the road hybrid vehicle.

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