scholarly journals Estimation of appropriate power range extender for battery electric vehicle

2019 ◽  
Vol 124 ◽  
pp. 02001
Author(s):  
A. N. Tikhomirov ◽  
O. B. Tikhomirova ◽  
M. E. Gnenik ◽  
V. Gropa
Keyword(s):  
Theoretical Study ◽  
Large City ◽  
Electricity Consumption ◽  
Driving Cycle ◽  
Urban Traffic ◽  
Battery Electric Vehicle ◽  
Traffic Conditions ◽  
Range Extender ◽  
Auxiliary Power ◽  
Curb Weight

The article presents the results of an experimental and theoretical study of the energy consumption of a vehicle under different conditions. The purpose was to determine the power of the auxiliary range extender on board of the electric battery vehicle. Driving is considered both for real road conditions of a large city, and a specific driving cycle. The high validity of the results is ensured by the use of the new driving cycle WLTC. It is shown that in urban traffic conditions 5 kW auxiliary power plant is sufficient for adequate compensation of electricity consumption of a vehicle with a curb weight 2500 kg.

scholarly journals Development of Driving Cycle Construction for Hybrid Electric Bus: A Case Study in Zhengzhou, China

2020 ◽  
Vol 12 (17) ◽  
pp. 7188
Author(s):  
Jiankun Peng ◽  
Jiwan Jiang ◽  
Fan Ding ◽  
Huachun Tan
Keyword(s):  
Transfer Matrix ◽  
Electricity Consumption ◽  
Original Data ◽  
Performance Measure ◽  
Driving Cycle ◽  
Statistical Characteristics ◽  
Hybrid Electric Bus ◽  
Navigation Function ◽  
Electric Bus ◽  
Hybrid Electric

A driving cycle is important to accomplish an accurate depiction of a vehicle’s driving characteristics as the traction motor’s flexible response to stop and start commands. In this paper, the driving cycle construction of an urban hybrid electric bus (HEB) in Zhengzhou, China is developed in which a measurement system integrating global positioning and inertial navigation function is used to acquire driving data. The collected data are then divided into acceleration, deceleration, uniform, and stop fragments. Meanwhile, the velocity fragments are classified into seven state clusters according to their average velocities. A transfer matrix applied to reveal the transfer relationship of velocity clusters can be obtained with statistical analysis. In the third stage, a three-part construction method of driving cycle is designed. Firstly, according to the theory of Markov chain, all the alternative parts that satisfy the construction’s precondition are selected based on the transfer matrix and Monte Carlo method. The Zhengzhou urban driving cycle (ZZUDC) could be determined by comparing the performance measure (PM) values subsequently. Eventually, the method and the cycle are validated by the high correlation coefficient (0.9972) with original data of ZZUDC than that of the other driving cycle (0.9746) constructed with traditional micro-trip and as well by comparing several statistical characteristics of ZZUDC and seven international cycles. Particularly, with around 20.5 L/100 km fuel and approximately 12.8 kwh/100 km electricity consumption, there is a narrow gap between the energy consumption of ZZUDC and WVUCITY, and their characteristics are similar.

scholarly journals Development of driving cycle under real world traffic conditions: A case study

2019 ◽  
Vol 9 (6) ◽  
pp. 4798
Author(s):  
Geetha A. ◽  
Subramani C.
Keyword(s):  
Driving Cycle ◽  
Time Dependent ◽  
Maximum Speed ◽  
Time Data ◽  
Maximum Acceleration ◽  
The Road ◽  
Traffic Conditions ◽  
Condensed Form ◽  
On The Road

<p><span>The modeling of a car is essentially done by taking into consideration the driving terrain, traffic conditions, driver’s behavior and various other factors which may directly or indirectly affect the vehicle’s performance. A vehicle is modeled for given specifications and constraints like maximum speed, maximum acceleration, and braking time, appropriate suspension for the gradient of the road and fuel consumption. Henceforth, a profound study and analysis of different drive cycles are essential. A time dependent drive cycle is a condensed form of data that helps us to determine the time taken to conduct the driving test on the road. This article highlights the development of a real driving cycle in the area of Tamilnadu, India. On-road vehicle’s speeds versus time data were obtained along the selected route. The data obtained were analyzed first and then a new driving cycle was developed.</span></p>

DESIGN OF COMBUSTOR FOR MICRO GAS TURBINE TEST RIG AND ITS PERFORMANCE PREDICTION

2015 ◽  
Vol 77 (8) ◽  
Author(s):  
Feng Xian Tan ◽  
Srithar Rajoo ◽  
Meng Soon Chiong ◽  
Cheng Tung Chong ◽  
Alessandro Romagnoli ◽  
...  
Keyword(s):  
Gas Turbine ◽  
Performance Prediction ◽  
Test Rig ◽  
Fuel Price ◽  
Power Generator ◽  
Micro Gas Turbine ◽  
Backup System ◽  
Range Extender ◽  
Auxiliary Power ◽  
And Performance

Stringent emission rules, air pollution, fluctuation of fuel price and depletion of fossil fuel resources are driving the industry to seek for better alternative of power generation. Micro gas turbine (MGT) provides a promising potential to solve the facing problems. MGT could be used in many applications such as in range extender vehicle, auxiliary power generator, power backup system, combine heat and power system, etc. Combustor plays a very crucial role in MGT system as its performance directly affects the emission quality, power output and fuel consumption of the entire system. This paper demonstrates the literature review, design methodology and performance prediction of the combustor designed for a 14.5kW MGT test rig.

scholarly journals TrafficPredict: Trajectory Prediction for Heterogeneous Traffic-Agents

2019 ◽  
Vol 33 ◽  
pp. 6120-6127 ◽  
Author(s):  
Yuexin Ma ◽  
Xinge Zhu ◽  
Sibo Zhang ◽  
Ruigang Yang ◽  
Wenping Wang ◽  
...  
Keyword(s):  
Autonomous Vehicles ◽  
Short Term Memory ◽  
Autonomous Vehicle ◽  
Large City ◽  
Urban Traffic ◽  
Heterogeneous Traffic ◽  
Prediction Algorithm ◽  
Trajectory Prediction ◽  
Long Short Term Memory ◽  
Traffic Agents

To safely and efficiently navigate in complex urban traffic, autonomous vehicles must make responsible predictions in relation to surrounding traffic-agents (vehicles, bicycles, pedestrians, etc.). A challenging and critical task is to explore the movement patterns of different traffic-agents and predict their future trajectories accurately to help the autonomous vehicle make reasonable navigation decision. To solve this problem, we propose a long short-term memory-based (LSTM-based) realtime traffic prediction algorithm, TrafficPredict. Our approach uses an instance layer to learn instances’ movements and interactions and has a category layer to learn the similarities of instances belonging to the same type to refine the prediction. In order to evaluate its performance, we collected trajectory datasets in a large city consisting of varying conditions and traffic densities. The dataset includes many challenging scenarios where vehicles, bicycles, and pedestrians move among one another. We evaluate the performance of TrafficPredict on our new dataset and highlight its higher accuracy for trajectory prediction by comparing with prior prediction methods.

Traffic Condition-Based Gain Scheduling of Electric Assist Control Strategy for Parallel Hybrid Electric Vehicles

2007 ◽  
Author(s):  
Amir Poursamad
Keyword(s):  
Fuel Consumption ◽  
Electric Vehicles ◽  
Control Strategy ◽  
Hybrid Electric Vehicles ◽  
Gain Scheduling ◽  
Driving Cycle ◽  
Traffic Condition ◽  
Traffic Conditions ◽  
Parallel Hybrid ◽  
Fuel Consumption And Emissions

This paper presents gain scheduling of control strategy for parallel hybrid electric vehicles based on the traffic condition. Electric assist control strategy (EACS) is employed with different parameters for different traffic conditions. The parameters of the EACS are optimized and scheduled for different traffic conditions of TEH-CAR driving cycle. TEH-CAR is a driving cycle which is developed based on the experimental data collected from the real traffic condition in the city of Tehran. The objective of the optimization is to minimize the fuel consumption and emissions over the driving cycle, while enhancing or maintaining the driving performance characteristics of the vehicle. Genetic algorithm (GA) is used to solve the optimization problem and the constraints are handled by using penalty functions. The results from the computer simulation show the effectiveness of the approach and reduction in fuel consumption and emissions, while ensuring that the vehicle performance is not sacrificed.

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