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Subjects -> ENERGY (Total: 414 journals) - ELECTRICAL ENERGY (12 journals) - ENERGY (252 journals) - ENERGY: GENERAL (7 journals) - NUCLEAR ENERGY (40 journals) - PETROLEUM AND GAS (58 journals) - RENEWABLE ENERGY (45 journals)
ENERGY: GENERAL (7 journals)

Showing 1 - 7 of 7 Journals sorted alphabetically
	Energy Research Journal (Followers: 3)
	Greenhouse Gases : Science and Technology (Followers: 4)
	International Journal of Energy Optimization and Engineering (Followers: 3)
	International Journal of Powertrains (Followers: 2)
	International Journal of Renewable Energy Development (Followers: 6)
	Journal of Power Technologies (Followers: 6)
	Journal of Thermophysics and Heat Transfer (Followers: 94)

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International Journal of Powertrains
Number of Followers: 2

Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1742-4267 - ISSN (Online) 1742-4275
Published by Inderscience Publishers

[439 journals]

Performance analysis of precise energy consumption algorithm for smart
home using hybrid renewable energy
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  Authors: Dinesh Kumar Anguraj, C. Sathya, C. Vinothini, M. Jayanthi, J. Vakula Rani
  Pages: 1 - 14
  Abstract: Energy demand is increased day by day, so there is a need for energy management, and it plays a vital part in the 21st century. At present, non-renewable energy is utilised most of the time. The people are not aware of the wastage of energy when their home appliances turned on the whole day. So, hybrid renewable energy is an alternate source for supplying continuous power for their smart home in the future. The collected renewable energy is utilised for the smart home usages. To minimise power consumption and to predict energy consumption precisely is a stimulating task in the future. This work proposes an innovative precise energy consumption algorithm (PECA) that is used to calculate the smart home power consumption accurately. PECA utilises smart plug, smart gateway, and mobile app administration platform to build and deploy a deep learning model. The smart plug and smart gateway combined into the complete distributed sensor network is to analyse, improve, and expand the energy consumption efficiently. Artificial intelligence is used to predict the energy usage data, optimise resource consumption, reduce the cost and maximise renewable energy usage.
  Keywords: renewable energy; precise energy consumption algorithm; PECA; smart home; smart plug; smart gateway; deep learning; artificial intelligence
  Citation: International Journal of Powertrains, Vol. 12, No. 1 (2023) pp. 1 - 14
  PubDate: 2023-03-20T23:20:50-05:00
  DOI: 10.1504/IJPT.2023.129658
  Issue No: Vol. 12, No. 1 (2023)
Transmission energy consumption analysis using ridge regression in
wireless sensor nodes
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  Authors: B. Rajesh Shyamala Devi, K. Sureshkumar, J.V. Anand
  Pages: 15 - 24
  Abstract: Wireless sensor nodes and its constitution of energy consumption in transferring sensory data to sink node results in a linear relationship. However, the quasi state of sensors makes over and under estimates of the communicating sensor data. This work proposes a 'ridge regression wireless data transfer rate' (RRWDTR) to interpret the energy analysis incorporating optimum route and its energy constituents, which relies on underlying resources. Since nodes are collocated to provide multi-hop communication, the independent variables of event generation are forwarding to a central node associated with sink results in multi-colinearity. The model thus deflates, leading to inaccurate estimates of prediction. RRWDTR uses structural relationship of the node and its resources using ridge regression incorporating characteristics of variance. Simulation of RRWDTR work has been done with penalty coefficient to predict near accurate estimates. Finally, comparison of RRWDTR has been done with linear regression wireless data transfer rate (LRWDTR).
  Keywords: ridge regression; energy conservation
  Citation: International Journal of Powertrains, Vol. 12, No. 1 (2023) pp. 15 - 24
  PubDate: 2023-03-20T23:20:50-05:00
  DOI: 10.1504/IJPT.2023.129662
  Issue No: Vol. 12, No. 1 (2023)
Deep-Q-network-based energy management of multi-resources in limited power
micro-grid
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  Authors: Nabil Jalil Aklo, Mofeed Turky Rashid
  Pages: 25 - 53
  Abstract: To overcome the shortage of power supply to the rural area, a hybrid connected mode micro-grid (MG) is proposed. It is suggested to include a diesel generator (DG) and renewable energy resources (RER) with a limited power of utility grid. To ensure the availability of fuel supply, the take-or-pay method is employed. In this paper, a smart energy management system (EMS) has been proposed to control the operation of hybrid MG, in addition to ensuring complete fuel disbursement under the scheduling of fuel supply. To facilitate the construction of EMS, a free model-based reinforcement learning (RL) algorithm has been employed for this purpose, in which the design of this algorithm depends on deep Q-network (DQN). The simulation of the algorithm has been achieved by MATLAB to validate the proposed system; the results showed a good performance of the technique compared with the performance achieved by improved particle swarm optimisation (IPSO) algorithm.
  Keywords: energy management system; renewable energy resources; reinforcement learning; deep Q-learning; take-or-pay
  Citation: International Journal of Powertrains, Vol. 12, No. 1 (2023) pp. 25 - 53
  PubDate: 2023-03-20T23:20:50-05:00
  DOI: 10.1504/IJPT.2023.129667
  Issue No: Vol. 12, No. 1 (2023)
Design and implementation of novel variants of multi-level inverters for
traction and heavy vehicle applications
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  Authors: S. Sri Krishna Kumar, P.K. Dhal
  Pages: 54 - 80
  Abstract: In the advent of increased transportation systems, the importance and growth of heavy vehicle movements and their applications play a significant role. The transportation sector involving heavy vehicles needs appropriate components for systematic movements. With the prominence of components utilised in heavy vehicle systems and power train mechanism, this paper develops novel variants of multi-level inverters for effective functioning of the vehicular system. Along with the classic multi-level inverter (MLI), new variants – modified multi-level inverter and reduced switch multi-level inverter are designed with complete operation based on their switching states and are implemented for a nine level output voltage module. The proposed inverter models for the heavy vehicle system is tested and validated based on the total harmonic distortion (THD). For each level and their switching states, THD is evaluated and the inverter performance is proved for its superiority for heavy vehicle system and their applicability in power train applications.
  Keywords: heavy vehicle system; component design; multi-level inverter; MLI; reduced switch multi-level inverter; total harmonic distortion; THD; switching states; power trains; duty cycle; switching losses; power quality
  Citation: International Journal of Powertrains, Vol. 12, No. 1 (2023) pp. 54 - 80
  PubDate: 2023-03-20T23:20:50-05:00
  DOI: 10.1504/IJPT.2023.129671
  Issue No: Vol. 12, No. 1 (2023)
Energy consumption analysis of extended-range electric vehicles with
different driving cycle and different modes
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  Authors: Yanlin Li, Dong Zhen, Yong Chen, Changyin Wei, Xiaozhe Lin
  Pages: 81 - 97
  Abstract: To reduce the energy consumption and extend the driving range of extended-range electric vehicles, an extended-range electric vehicle with different modes is employed to analyse the energy consumption characteristics and driving range under different driving cycle. The vehicle working mode is divided into hybrid mode and pure electric mode. The results show that under CLTC, the difference between the actual speed and the required speed in the two operating modes is the smallest. Driving resistance is the main factor that affects the energy consumption and driving range of the vehicle; brake recovery also has a certain impact on driving mileage. In the pure electric mode, the driving range of the vehicle is the longest under the NEDC. And in the hybrid mode, the engine loses the highest energy. These results are helpful for the study of energy consumption law of extended-range electric vehicles and the design of energy management strategies.
  Keywords: extended range electric vehicles; energy consumption; driving cycle; energy management
  Citation: International Journal of Powertrains, Vol. 12, No. 1 (2023) pp. 81 - 97
  PubDate: 2023-03-20T23:20:50-05:00
  DOI: 10.1504/IJPT.2023.129673
  Issue No: Vol. 12, No. 1 (2023)