scholarly journals Consumption Evaluation of Energy Consumption and Emissions of BAS KITe in Kuala Terengganu from the Development of Its Driving Cycle

2020 ◽  
Vol 1532 ◽  
pp. 012018
Author(s):  
I.N. Anida ◽  
J.S. Norbakyah ◽  
M. Zulfadli ◽  
M.H. Norainiza ◽  
A.R. Salisa
2018 ◽  
Vol 26 (14) ◽  
pp. 13839-13853 ◽  
Author(s):  
Xuan Zhao ◽  
Jian Ma ◽  
Shu Wang ◽  
Yiming Ye ◽  
Yan Wu ◽  
...  

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 55586-55598 ◽  
Author(s):  
Klaus Kivekas ◽  
Jari Vepsalainen ◽  
Kari Tammi

Energies ◽  
2020 ◽  
Vol 13 (18) ◽  
pp. 4639 ◽  
Author(s):  
Anatole Desreveaux ◽  
Alain Bouscayrol ◽  
Elodie Castex ◽  
Rochdi Trigui ◽  
Eric Hittinger ◽  
...  

The energy consumption of an electric vehicle is primarily due to the traction subsystem and the comfort subsystem. For a regular trip, the traction energy can be relatively constant but the comfort energy has variation depending on seasonal temperatures. In order to plan the annual charging operation of an eco-campus, a simulation tool is developed for an accurate determination of the consumption of an electric vehicle throughout year. The developed model has been validated by comparison with experimental measurement of a real vehicle on a real driving cycle. Different commuting trips are analyzed over a complete year. For the considered city in France (Lille), the comfort energy consumption has an overconsumption up to 33% in winter due to heating, and only 15% in summer due to air conditioning. The urban commuting driving cycle is more affected by the comfort subsystem than extra-urban trips.


2020 ◽  
Vol 5 (2) ◽  
pp. 266-285
Author(s):  
Levente Czégé ◽  
Attila Vámosi ◽  
Imre Kocsis

The goal of this paper is to give an overview of the literature of construction techniques of driving cycles. Our motivation for the overview is the future goal of constructing our own driving cycles for various types of vehicles and routes. This activity is part of a larger project focusing on determination of fuel and energy consumption by dynamic simulation of vehicles. Accordingly, the papers dealing with sample route determination, data collection and processing, driving cycle construction procedures, statistical evaluation of data are in our focus.


2018 ◽  
Vol 10 (11) ◽  
pp. 168781401880923
Author(s):  
Yuefei Wang ◽  
Nan Zhang ◽  
Ye Wu ◽  
Baijun Liu ◽  
Yuan Wu

Electrical energy consumption is an important component of energy consumption for internal combustion engine vehicle, which directly affects the fuel economy. A bus-based electrical energy management system is built, and an electrical energy management strategy based on driving cycle recognition and electrical load perception is presented to achieve the refined management of vehicle energy. Six typical driving cycles are selected to establish an improved learning vector quantization neural network model for driving cycle recognition. The corresponding model training algorithm is designed by utilizing a similar driving cycle classification and the gradient optimization so that the better recognition accuracy and the less computation intensity can be obtained. An online recognition mechanism based on sliding time window is devised, and the optimal time window length is determined. To minimize fuel consumption, a dynamic optimal regulation strategy for the output power of the alternator and battery, which considers driving cycle recognition and electrical load perception, is proposed. Experimental results show that the strategy can effectually improve the vehicle fuel economy according to the driving cycle and the electrical load change and decrease the fuel consumption per 100 miles of vehicle.


Author(s):  
I N Anida ◽  
J S Norbakyah ◽  
M Zulfadli ◽  
M H Norainiza ◽  
A R Salisa

Energies ◽  
2019 ◽  
Vol 12 (6) ◽  
pp. 1122 ◽  
Author(s):  
Xiaogang Wu ◽  
Dianyu Zheng ◽  
Tianze Wang ◽  
Jiuyu Du

All-wheel drive is an important technical direction for the future development of pure electric vehicles. The difference in the efficiency distribution of the shaft motor caused by the optimal load matching and motor manufacturing process, the traditional torque average distribution strategy is not applicable to the torque distribution of the all-wheel drive power system. Aiming at the above problems, this paper takes the energy efficiency of power system as the optimization goal, proposes a dynamic allocation method to realize the torque distribution of electric vehicle all-wheel drive power system, and analyzes and verifies the adaptability of this optimization algorithm in different urban passenger vehicle working cycles. The simulation results show that, compared with the torque average distribution method, the proposed method can effectively solve the problem that the difference of the efficiency distribution of the two shaft motors in the power system affects the energy consumption of the power system. The energy consumption rate of the proposed method is reduced by 5.96% and 5.69%, respectively, compared with the average distribution method under the China urban passenger driving cycle and the Harbin urban passenger driving cycle.


2021 ◽  
Vol 1068 (1) ◽  
pp. 012008
Author(s):  
E.A.E.S Shahiran ◽  
I.N. Anida ◽  
J.S. Norbakyah ◽  
A.R. Salisa

Energies ◽  
2021 ◽  
Vol 14 (9) ◽  
pp. 2592
Author(s):  
Iwona Komorska ◽  
Andrzej Puchalski ◽  
Andrzej Niewczas ◽  
Marcin Ślęzak ◽  
Tomasz Szczepański

A driving cycle is a time series of a vehicle’s speed, reflecting its movement in real road conditions. In addition to certification and comparative research, driving cycles are used in the virtual design of drive systems and embedded control algorithms, traffic management and intelligent road transport (traffic engineering). This study aimed to develop an adaptive driving cycle for a known route to optimize the energy consumption of an electric vehicle and improve the driving range. A novel distance-based adaptive driving cycle method was developed. The proposed algorithm uses the segmentation and iterative synthesis procedures of Markov chains. Energy consumption during driving is monitored on an ongoing basis using Gaussian process regression, and speed and acceleration are corrected adaptively to maintain the planned energy consumption. This paper presents the results of studies of simulated driving cycles and the performance of the algorithm when applied to the real recorded driving cycles of an electric vehicle.


Sign in / Sign up

Export Citation Format

Share Document