Investigation of energy usage and emissions on plug-in and hybrid electric vehicle

Barmaki, Reza; Ilkhani, Mohammad Reza; Salehpour, Saman

doi:10.17559/tv-20140928112417

Cited by 4 publications

(4 citation statements)

References 24 publications

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“…In areas where the price of power is greater during peak hours and cheaper during off-peak hours, this technique can be highly beneficial. By avoiding power use at peak times and charging the BRV battery during off-peak times, the system saves money for the customer [6]. The machine learning system learns about the appropriate usage of batteries depending on the existing power tariffs and the household load.…”

Section: Introductionmentioning

confidence: 99%

Heuristic Adaptive Dynamic Programming-based Energy Optimization Strategies for Hybrid Electric Vehicles

2024

Teh. vjesn.

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This study suggests a more accurate series-parallel hybrid electric vehicle (HEV) energy management control technique based on heuristic adaptive dynamic programming (HADP). This article provides a solution to the energy management problem of Electric Vehicle using the Stimulant Dependent Heuristic Adaptive Dynamic Programming (SDHADP) algorithm. The reinforced Q learning model along with the three-layer feed-forward neural network with backpropagation is detailed with the necessary diagrams and equations. The approach to connecting the battery of a BEV to a residential system is novel and innovative. The experimental results highlight the importance and effectiveness of the SDHADP algorithm using an electric vehicle. This is done with the help of a BV feed-forward neural network with back-propagation. In the model, which is in charge of controlling the system based on the training given by the BV network, Markov decision theory was adopted by scholars as a paradigm for posing and solving planning problems in the face of uncertainty and is in charge of the battery management system.The suggested technique uses online learning control methods to improve upon the efficiency of the traditional Q-learning method by more than 4.55% under the specified real-world driving circumstances.

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Section: Introductionmentioning

confidence: 99%

Heuristic Adaptive Dynamic Programming-based Energy Optimization Strategies for Hybrid Electric Vehicles

2024

Teh. vjesn.

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“…They observed that an HEV contains an internal combustion engine and a fuel tank, and an electric motor and an ESS (battery). In summary, they stated that in an HEV, they used two drivers for the powertrain [3]. In conventional vehicles, kinetic energy is lost as heat energy in the lining in the case of sudden braking or downhill idling.…”

Section: Introductionmentioning

confidence: 99%

Efficiency Analysis of Regenerative Brake System Using Flywheel Energy Storage Technology in Electric Vehicles

2024

Teh. vjesn.

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The increase in fossil fuel consumption used in conventional vehicles has adversely affected carbon emissions in the atmosphere. Due to this negativity, many problems such as global warming, noise pollution, and cost have emerged. To find solutions to these problems, many studies have been conducted to increase the energy storage capacity of Electric Vehicles (EVs) since 1835. EVs produced as a result of these studies work more efficiently than traditional vehicles. However, the driving range problem and charging time are the biggest disadvantages of these vehicles. These disadvantages are a major obstacle for EVs to replace traditional vehicles. In this study, an experimental study was conducted on flywheel-battery in-vehicle topologies, which are recommended to increase the range of EVs and hybrid electric vehicles. In this application, two flywheels with the same rotor radius and different masses were used. Energy was produced by the generator through these flywheels. This energy was employed to charge the batteries. The stored energy and power amounts were investigated depending on the variation in the moment of inertia of both flywheels at the maximum and minimum levels. Because of this examination, it has been determined which flywheels with the same rotor radius but different masses are more suitable for EVs.

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“…The sustainability of the transport system is increasingly becoming a top-priority objective of public administrations at all levels. At strategic levels, these policy objectives are mainly driven by international commitments to reducing greenhouse gas (GHG) emissions [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Drag Reduction of Semi-Trailer Aerodynamic Devices Determined by Two Different Methods

et al. 2020

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Actual trend in transport is the decreasing of the energy consumption and GHG production. One way how to decrease these two attributes is the reduction of aerodynamic drag using additional aerodynamic devices on semi-trailers. The paper presents the results of their influence measured by using two different methods. The results were obtained by performing coastdown tests and they were compared with outputs obtained by the measurements in the wind tunnel. The driving test consists of the coastdown test on flat straight track protected from the wind impact. The current velocity was measured by the GPS sensor. The vehicle deceleration was used for the acting driving resistances counting-one of them is the air drag-drag coefficient. The results were compared with drag coefficient values examined in a wind tunnel test on a scaled truck model done by authors.

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Investigation of energy usage and emissions on plug-in and hybrid electric vehicle

Cited by 4 publications

References 24 publications

Heuristic Adaptive Dynamic Programming-based Energy Optimization Strategies for Hybrid Electric Vehicles

Heuristic Adaptive Dynamic Programming-based Energy Optimization Strategies for Hybrid Electric Vehicles

Efficiency Analysis of Regenerative Brake System Using Flywheel Energy Storage Technology in Electric Vehicles

Drag Reduction of Semi-Trailer Aerodynamic Devices Determined by Two Different Methods

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