A Simulation Study of Hybrid Electric Hummer H3: Effects of Drive Train Hybridization on Performance and Fuel Economy

Author(s):  
M. Bhatia ◽  
O. Tisler ◽  
N. Panchal ◽  
M. Ozcan ◽  
B. Seaton ◽  
...  
Author(s):  
Lei Feng ◽  
Bo Chen

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.


2008 ◽  
Author(s):  
Samaneh Haddadi ◽  
Vahid Esfahanian ◽  
Hassan Nehzati ◽  
Farhad Sangtarash ◽  
Arash Akhgari

2021 ◽  
Vol 292 ◽  
pp. 126040
Author(s):  
Xiaohua Zeng ◽  
Qifeng Qian ◽  
Hongxu Chen ◽  
Dafeng Song ◽  
Guanghan Li

2011 ◽  
Vol 121-126 ◽  
pp. 2710-2714
Author(s):  
Ling Cai ◽  
Xin Zhang

With the requirements for reducing emissions and improving fuel economy, it has been recognized that the electric, hybrid electric powered drive train technologies are the most promising solution to the problem of land transportation in the future. In this paper, the parameters of series hybrid electric vehicle (SHEV), including engine-motor, battery and transmission, are calculated and matched. Advisor software is chosen as the simulation platform, and the major four parameters are optimized in orthogonal method. The results show that the optimal method and the parameters can improve the fuel economy greatly.


2012 ◽  
Vol 546-547 ◽  
pp. 212-217
Author(s):  
Xu Dong Wang ◽  
Hai Xing Zhang ◽  
Shu Cai Yang ◽  
Yong Qin Zhou ◽  
Jin Fa Liu

Based on the configuration and working state analysis of the ISG hybrid electric cars, the torque distribution strategy of a hybrid system is designed to delineate the maximum and minimum work torque curves of the engine, achieve optimization of engine’s range so as to make sure the target torque of the engine and ISG motor, and finally through the calibrated driving characteristics MAP and battery SOC state to achieve the calculation of total vehicle torque demand. Taking the Hafei Saibao ISG hybrid car as a test model, the test of fuel economy and emissions carried out under specific conditions showed that using the torque distribution strategy has increased by 12.8 % of the ISG hybrid car fuel economy and improved emissions performance to some extent compared to the traditional Hafei Saibao cars.


Author(s):  
Tao Deng ◽  
Ke Zhao ◽  
Haoyuan Yu

In the process of sufficiently considering fuel economy of plug-in hybrid electric vehicle (PHEV), the working time of engine will be reduced accordingly. The increased frequency that the three-way catalytic converter (TWCC) works in abnormal operating temperature will lead to the increasing of emissions. This paper proposes the equivalent consumption minimization strategy (ECMS) to ensure the catalyst temperature of PHEV can work in highly efficient areas, and the influence of catalyst temperature on fuel economy and emissions is considered. The simulation results show that the fixed equivalent factor of ECMS has great limitations for the underutilized battery power and the poor fuel economy. In order to further reduce fuel consumption and keep the emission unchanged, an equivalent factor map based on initial state of charge (SOC) and vehicle mileage is established by the genetic algorithm. Furthermore, an Adaptive changing equivalent factor is achieved by using the following strategy of SOC trajectory. Ultimately, adaptive equivalent consumption minimization strategy (A-ECMS) considering catalyst temperature is proposed. The simulation results show that compared with ordinary ECMS, HC, CO, and NOX are reduced by 14.6%, 20.3%, and 25.8%, respectively, which effectively reduces emissions. But the fuel consumption is increased by only 2.3%. To show that the proposed method can be used in actual driving conditions, it is tested on the World Light Vehicle Test Procedure (WLTC).


Author(s):  
Naser Sina ◽  
Vahid Esfahanian ◽  
Mohammad Reza Hairi Yazdi

Plug-in hybrid electric buses are a viable solution to increase the fuel economy. In this framework, precise estimation of optimal state-of-charge trajectory along the upcoming driving cycle appears to play a pivotal role in the way to approach the globally optimal fuel economy. This paper aims to conduct a parametric study on the key factors affecting the estimation of optimal state-of-charge trajectory, including trip information availability and trip segment distance, and to provide a guideline for the design and implementation of predictive energy management systems. To accomplish this, the dynamic programming algorithm is employed to obtain the solution of optimal control problem for the sampled driving cycles in a particular bus route. A large database comprising of driving features of the cycles and the optimal solution is developed which then is used to construct a neural network based estimator for obtaining the optimal state-of-charge trajectory. The main results show promising performance of the proposed method with about 76% reduction in the root mean square error of the estimated trajectory comparing to the linear state-of-charge trajectory assumption. Moreover, the robustness of the estimator is verified through simulation and it is observed that appropriate choice of trip segment distance is vital to improve the estimation accuracy, especially in case of uncertain prediction of trip information.


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