scholarly journals The influence of driving pattern on pollutant emission and fuel consumption of hybrid electric vehicle

2019 ◽  
Vol 177 (2) ◽  
pp. 145-150
Author(s):  
Jakub LASOCKI ◽  
Maciej GIS
Keyword(s):  
Fuel Consumption ◽  
The Other ◽  
Pollutant Emission ◽  
Valuable Insight ◽  
Vehicle Speed ◽  
Actual Performance ◽  
Driving Cycles ◽  
Driving Pattern ◽  
Hybrid Electric ◽  
The Impact

Hybrid electric vehicles (HEVs) have an increasing presence in passenger transport segment. They have been designed to minimize energy consumption and pollutant emission. However, the actual performance of HEVs depends on the dynamic conditions in which they are used, and vehicle speed is one of the key factors. A lot of excess emission and fuel consumption can be attributed to rapid changes of vehicle speed, i.e. accelerations and decelerations. On the other hand, dynamic driving favours energy recovery during braking. This study examines the relationship between HEVs speed, pollutant emission and fuel consumption. The considerations were based on the results of testing vehicles in WLTC and NEDC driving cycles, performed on a chassis dynamometer. The test objects were two light-duty passenger vehicles, one with series-parallel, gasoline-electric hybrid system and the other, used as a reference, with conventional sparkignition engine. Both vehicles had similar technical parameters and combustion engines supplied with gasoline. The driving cycles were divided into several parts according to the speed range. For each part, pollutant emission and fuel consumption were determined and appropriate values of selected parameters of driving pattern were calculated. Combining the results of empirical research and calculated parameters allowed to obtain characteristics. Their analysis provided valuable insight into the impact of driving pattern on actual emission and fuel consumption of HEV.

scholarly journals Optimization of Aircraft Climb Trajectory considering Environmental Impact under RTA Constraints

2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Junqiang Wan ◽  
Honghai Zhang ◽  
Fangzi Liu ◽  
Wenying Lv ◽  
Yifei Zhao
Keyword(s):  
Fuel Consumption ◽  
Greenhouse Effect ◽  
Time Window ◽  
Optimization Method ◽  
Warming Trend ◽  
Weighting Factor ◽  
Pollutant Emission ◽  
Sustainable Operation ◽  
Aircraft Performance ◽  
The Impact

In order to realize the concept of air traffic sustainable operation, taking the aircraft climbing stage as an example, firstly, we establish the vertical trajectory model of aircraft climbing, analyze the change rule of aircraft performance parameters under different indicated airspeed, and establish the RTA and RHA constraint models according to the waypoint constraints. Then, considering the fuel economy and the greenhouse effect of pollutant emission, we establish a multiobjective model of aircraft flight parameter optimization, and, based on the multiobjective genetic algorithm, we establish an optimization model. Finally, we use B737-800 aircraft to carry out simulation experiments and find that, with the change of speed, fuel consumption and warming trend are different, and “objective weight, aircraft mass, flight distance, RTA time window, and wind” have different effects on the optimization results. The results show that this optimization method has a good compromise between fuel consumption and greenhouse effect by changing the weighting factor. By optimizing the flight parameters of the aircraft, it can effectively reduce the impact on the environment and provide theoretical support for the green flight of the aircraft.

Efficiency improvement of a novel dual motor powertrain for plug-in hybrid electric buses

2020 ◽  
Vol 234 (7) ◽  
pp. 1869-1882 ◽  
Author(s):  
Cong Thanh Nguyen ◽  
Paul D Walker ◽  
Nong Zhang ◽  
Jiageng Ruan
Keyword(s):  
Dynamic Programming ◽  
Vehicle Speed ◽  
Efficiency Improvement ◽  
Energy Management Strategy ◽  
Power Sources ◽  
Single Motor ◽  
Driving Cycles ◽  
Parallel Hybrid ◽  
Hybrid Electric ◽  
Powertrain Configuration

Powertrain configuration plays an important role in the performance of plug-in hybrid electric buses. Current designs are the compromise between energy efficiency, dynamic ability, shifting smoothness and manufactural cost. To balance the above requirements, this research proposes a novel dual motor powertrain for plug-in hybrid electric buses. The efficiency improvement is compared to the conventional plug-in parallel hybrid electric buses with a single motor powertrain. Parameter designs of system components guarantee two configurations equivalently. To maximize the benefits of the proposed powertrain, this paper introduces an energy management strategy which coordinates enumeration method and dynamic programming to build the optimal maps of powertrain operation. The enumeration method determines the working points of power sources and gear states in all possible modes according to vehicle speed and power. The dynamic programming then selects the most suitable mode with the consideration of gear shifting and mode change in the optimal maps. Simulation results show that the dual motors work in peak efficiency region much more frequently than the single motor in different conditions. Therefore, the total energy cost of dual motor powertrain for entire driving cycles decreases significantly in comparison with the single motor powertrain, 6.5% in the LA92 and 6.7% in the Urban Dynamometer Driving Schedule.

scholarly journals Research on energy management strategy considering battery life for plug-in hybrid electric vehicle

2018 ◽  
Vol 10 (9) ◽  
pp. 168781401879776 ◽  
Author(s):  
Jianjun Hu ◽  
Zhihua Hu ◽  
Xiyuan Niu ◽  
Qin Bai
Keyword(s):  
Fuel Consumption ◽  
Electric Vehicle ◽  
Energy Management ◽  
Management Strategy ◽  
Hybrid Electric Vehicle ◽  
Battery Life ◽  
Energy Management Strategy ◽  
Driving Cycles ◽  
Comprehensive Test ◽  
Hybrid Electric

To improve the fuel efficiency and battery life-span of plug-in hybrid electric vehicle, the energy management strategy considering battery life decay is proposed. This strategy is optimized by genetic algorithm, aiming to reduce the fuel consumption and battery life decay of plug-in hybrid electric vehicle. Besides, to acquire better drive-cycle adaptability, driving patterns are recognized with probabilistic neural network. The standard driving cycles are divided into urban congestion cycle, highway cycle, and urban suburban cycle; the optimized energy management strategies in three representative driving cycles are established; meanwhile, a comprehensive test driving cycle is constructed to verify the proposed strategies. The results show that adopting the optimized control strategies, fuel consumption, and battery’s life decay drop by 1.9% and 3.2%, respectively. While using the drive-cycle recognition, the features of different driving cycles can be identified, and based on it, the vehicle can choose appropriate control strategy in different driving conditions. In the comprehensive test driving cycle, after recognizing driving cycles, fuel consumption and battery’s life decay drop by 8.6% and 0.3%, respectively.

scholarly journals Real-Time Control Strategy for CVT-Based Hybrid Electric Vehicles Considering Drivability Constraints

2019 ◽  
Vol 9 (10) ◽  
pp. 2074 ◽  
Author(s):  
Hangyang Li ◽  
Yunshan Zhou ◽  
Huanjian Xiong ◽  
Bing Fu ◽  
Zhiliang Huang
Keyword(s):  
Real Time ◽  
Fuel Consumption ◽  
Electric Vehicles ◽  
Hybrid Electric Vehicles ◽  
Continuously Variable Transmission ◽  
Speed Ratio ◽  
Driving Pattern ◽  
Variable Transmission ◽  
Equivalent Factor ◽  
Hybrid Electric

The energy management strategy has a great influence on the fuel economy of hybrid electric vehicles, and the equivalent consumption minimization strategy (ECMS) has proved to be a useful tool for the real-time optimal control of Hybrid Electric Vehicles (HEVs). However, the adaptation of the equivalent factor poses a major challenge in order to obtain optimal fuel consumption as well as robustness to varying driving cycles. In this paper, an adaptive-ECMS based on driving pattern recognition (DPR) is established for hybrid electric vehicles with continuously variable transmission. The learning vector quantization (LVQ) neural network model was adopted for the on-line DPR algorithm. The influence of the battery state of charge (SOC) on the optimal equivalent factor was studied under different driving patterns. On this basis, a method of adaptation of the equivalent factor was proposed by considering the type of driving pattern and the battery SOC. Besides that, in order to enhance drivability, penalty terms were introduced to constrain frequent engine on/off events and large variations of the continuously variable transmission (CVT) speed ratio. Simulation results showed that the proposed method efficiently improved the equivalent fuel consumption with charge-sustaining operations and also took into account driving comfort.

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.

scholarly journals Driving Cycles Based on Fuel Consumption

Energies ◽  
2018 ◽  
Vol 11 (11) ◽  
pp. 3064 ◽  
Author(s):  
José Huertas ◽  
Michael Giraldo ◽  
Luis Quirama ◽  
Jenny Díaz
Keyword(s):  
Fuel Consumption ◽  
Test Procedure ◽  
Region Of Interest ◽  
Driving Cycle ◽  
Normal Operation ◽  
Characteristic Parameters ◽  
Driving Cycles ◽  
Driving Pattern ◽  
Relative Differences ◽  
Fuel Consumption And Emissions

Type-approval driving cycles currently available, such as the Federal Test Procedure (FTP) and the Worldwide harmonized Light vehicles Test Cycle (WLTC), cannot be used to estimate real fuel consumption nor emissions from vehicles in a region of interest because they do not describe its local driving pattern. We defined a driving cycle (DC) as the time series of speeds that when reproduced by a vehicle, the resulting fuel consumption and emissions are similar to the average fuel consumption and emissions of all vehicles of the same technology driven in that region. We also declared that the driving pattern can be described by a set of characteristic parameters (CPs) such as mean speed, positive kinetic energy and percentage of idling time. Then, we proposed a method to construct those local DC that use fuel consumption as criterion. We hypothesized that by using this criterion, the resulting DC describes, implicitly, the driving pattern in that region. Aiming to demonstrate this hypothesis, we monitored the location, speed, altitude, and fuel consumption of a fleet of 15 vehicles of similar technology, during 8 months of normal operation, in four regions with diverse topography, traveling on roads with diverse level of service. In every region, we considered 1000 instances of samples made of m trips, where m varied from 4 to 40. We found that the CPs of the local driving cycle constructed using the fuel-based method exhibit small relative differences (<15%) with respect to the CPs that describe the driving patterns in that region. This result demonstrates the hypothesis that using the fuel based method the resulting local DC exhibits CPs similar to the CPs that describe the driving pattern of the region under study.

scholarly journals The Impact of Pre-Competition Estimated Results for Elite Archers on Performance According to Achievement Goal Theory

2018 ◽  
Vol 8 (4) ◽  
pp. 146
Author(s):  
Utku Isik
Keyword(s):  
Goal Orientation ◽  
Achievement Goal ◽  
The Other ◽  
Achievement Goal Theory ◽  
Goal Theory ◽  
Actual Performance ◽  
Survey Form ◽  
Ego Orientation ◽  
The Impact ◽  
The Relationship

This study aimed to evaluate the estimated/judged results for elite archers before competitions in the context ofAchievement Goal Theory and determine its impact on actual performance. Also, the study assessed the impact ofgoal orientation on the competition scores to comprehend the relationship between goal orientation and performance.Study participants were 116 elite archers who participated in Adult-Youth Indoor Turkey Championship in Izmir.Before the competition, the participants filled in the “Task and Ego Orientation in Sport Questionnaire”. Before thisscale, a survey form, developed by the researcher, was given to participants to learn about their personalcharacteristics. The participants were asked to make a note of the numbers they wore on the chest of their uniformsand the predicted/judged scores on the survey form. They were informed that at the termination of the competition,their actual scores and their predicted scores would be compared. The study presents two important results. The firstresult is related to the fact that athletes with high goal orientation were significantly more successful than those withlow goal orientation in a real competition environment. The other result of in the current study was the significantrelationship between the pre-competition predictions/estimates of individuals with high goal orientation and theircompetition performances. This study is significant because it demonstrated that individuals with higher goalorientations have higher performances and that their predictions/estimates for their performance are much moreaccurate.

Analysis of Driving Cycles for Emission Test of Light-Duty Vehicles in China

2012 ◽  
Vol 616-618 ◽  
pp. 1154-1160
Author(s):  
Jin Lin Xue
Keyword(s):  
Real World ◽  
Gasoline Engine ◽  
The Real ◽  
Driving Behaviors ◽  
Traffic Conditions ◽  
Driving Cycles ◽  
Driving Pattern ◽  
Light Duty ◽  
Light Duty Vehicles ◽  
The Impact

The driving cycles employed to measure the emissions from automotive vehicles should adequately represent the real-world driving pattern of the vehicle to provide the most realistic estimation of emissions levels. The driving cycles used for light-duty gasoline engine vehicles in China were reviewed in this paper firstly. Then the impact of various factors, such as driving behaviors, driving conditions, road conditions, traffic conditions, on real-world emission levels were analyzed. Finally, the shortages of the existing driving cycles were pointed out. It can be concluded that the emissions levels from automotive vehicles are underestimated because of the characteristics of the existing drive cycles, so it is urgent to research and develop new driving cycles to fit the situation of China.

scholarly journals Methods and simulation to reduce fuel consumption in driving cycles for category N1 motor vehicles

2021 ◽  
Author(s):  
Alexander Kolin ◽  
S. E. Silantyev ◽  
Petr Rogov ◽  
M. E. Gnenik
Keyword(s):  
Fuel Consumption ◽  
Impact Analysis ◽  
Motor Vehicle ◽  
Commercial Vehicle ◽  
Road Traffic ◽  
Motor Vehicles ◽  
Design Parameters ◽  
Driving Cycles ◽  
Light Commercial Vehicle ◽  
The Impact

The paper presents the results of using the simulation model estimating the fuel consumption of a light commercial vehicle in road traffic cycles; virtual tests are performed. The impact analysis of the motor vehicle design parameters on fuel consumption in NEDC and WLTC cycles is conducted. Numerical values of average fuel consumption are obtained for variation of the main parameters of the structure in NEDC and WLTC cycles. Energy distribution is shown during the motion of category N1 light commercial vehicle.

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