Institution
National University of Malaysia
Education•Kuala Lumpur, Malaysia•
About: National University of Malaysia is a education organization based out in Kuala Lumpur, Malaysia. It is known for research contribution in the topics: Population & Heat transfer. The organization has 26593 authors who have published 41270 publications receiving 552683 citations. The organization is also known as: NUM & Universiti Kebangsaan Malaysia.
Topics: Population, Heat transfer, Thin film, Membrane, Photovoltaic system
Papers published on a yearly basis
Papers
More filters
••
TL;DR: Total lipids extracted from 20 species of freshwater fish in Malaysia were analyzed for their total fat and fatty acids composition and showed that total monounsaturated fatty acids were the highest, followed by saturated and polyuns saturated fatty acids.
196 citations
••
TL;DR: In this paper, the authors presented the outcome of a new system architecture and control algorithm that can use both battery storage and manage the temperature of thermal appliances, which is an important part of the smart grid that enables residential customers to execute demand response programs autonomously.
195 citations
••
TL;DR: In this article, the performance analysis of solar drying system for red chili was concerned with performance analysis, energy and exergy analyses of the solar drying process were performed, and the results showed that the efficiency was 28, 13, 45, and 57% at an average solar radiation of 420 W/m2 and a mass flow rate of 0.07
195 citations
••
TL;DR: The authors examines the extent to which the religious factor has bearing on policy and development strategy affecting tourism in Muslim countries and suggests that, although the doctrine of Islam encourages travel and hospitable behavior, it has little influence on the mode of tourism development.
194 citations
••
TL;DR: The obtained results show that the BPNN based BSA model outperforms other neural network models in estimating SOC with high accuracy under different EV profiles and temperatures.
Abstract: The state of charge (SOC) is a critical evaluation index of battery residual capacity. The significance of an accurate SOC estimation is great for a lithium-ion battery to ensure its safe operation and to prevent from over-charging or over-discharging. However, to estimate an accurate capacity of SOC of the lithium-ion battery has become a major concern for the electric vehicle (EV) industry. Therefore, numerous researches are being conducted to address the challenges and to enhance the battery performance. The main objective of this paper is to develop an accurate SOC estimation approach for a lithium-ion battery by improving back-propagation neural network (BPNN) capability using backtracking search algorithm (BSA). BSA optimization is utilized to improve the accuracy and robustness of BPNN model by finding the optimal value of hidden layer neurons and learning rate. In this paper, Dynamic Stress Test and Federal Urban Driving Schedule drive profiles are applied for testing the model at three different temperatures. The obtained results of the BPNN based BSA model are compared with the radial basis function neural network, generalized regression neural network and extreme learning machine model using statistical error values of root mean square error, mean absolute error, mean absolute percentage error, and SOC error to check and validate the model performance. The obtained results show that the BPNN based BSA model outperforms other neural network models in estimating SOC with high accuracy under different EV profiles and temperatures.
194 citations
Authors
Showing all 26827 results
Name | H-index | Papers | Citations |
---|---|---|---|
Jonathan E. Shaw | 114 | 629 | 108114 |
Sabu Thomas | 102 | 1554 | 51366 |
Biswajeet Pradhan | 98 | 735 | 32900 |
Haji Hassan Masjuki | 97 | 502 | 29653 |
Mika Sillanpää | 96 | 1019 | 44260 |
Choon Nam Ong | 86 | 444 | 25157 |
Keith R. Abrams | 86 | 355 | 30980 |
Kamaruzzaman Sopian | 84 | 989 | 25293 |
Benedikt M. Kessler | 82 | 385 | 24243 |
Michel Marre | 82 | 444 | 39052 |
Peter Willett | 76 | 479 | 29037 |
Peter F. M. Choong | 72 | 532 | 18185 |
Nidal Hilal | 72 | 395 | 21524 |
Margareta Nordin | 72 | 267 | 19578 |
Teuku Meurah Indra Mahlia | 70 | 339 | 17444 |