Power Management Strategy for Battery Electric Vehicles

Saleeb, Hedra; Sayed, Khairy; Kassem, Ahmed M.; Mostafa, R.

doi:10.1049/iet-est.2018.5026

Cited by 26 publications

(20 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This turns on the electric motor to transfer the brake energy to the battery and charge it for 4S. At the time t 16 s, the accelerator is suddenly pushed to 70% again [41,42].…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

Artificial neural networks applied on induction motor drive for an electric vehicle propulsion system

2021

Self Cite

View full text Add to dashboard Cite

This article presents an artificial neural network (ANN) for loss minimization of direct torque-controlled induction motor (IM)-driven electric vehicles. The IM drive can consume more power than it needs to be, especially when it is operating under conditions under full load. The proposed architecture and its control strategies use ANN to control the amplitude starting current and save more power. The performance of these controllers was verified by simulation using the MATLAB/SIMULINK package, and the results showed good and high performance in the time domain response and rapid rejection of the system disturbance compared to the conventional proportional-integral derivative (PID) controller. Thus, the core loss of IM greatly reduces, thus improving the efficiency of the driving system. Finally, the experimental results were validated which were highly consistent with the simulation results using the DSPACE MicroLabBox controller.

show abstract

“…This turns on the electric motor to transfer the brake energy to the battery and charge it for 4S. At the time t 16 s, the accelerator is suddenly pushed to 70% again [41,42].…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

Artificial neural networks applied on induction motor drive for an electric vehicle propulsion system

2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…There are no tailpipe emissions for electric vehicles because they are propelled by electric motors instead of internal combustion engines, even though there are many obstacles related to technology and infrastructure against the widespread adoption of electric vehicles [18]. These challenges include lack of infrastructure for charging electric vehicles [19], charging time [20], driving range [21], high price [22], and uncertainties associated with the potential benefits of these vehicles [23]. Electric vehicles have great potential to minimize the external impacts of road transportation, including air pollution [24] and associated health impacts on urban population [25], global climate change [26] and so on.…”

Section: Study On Electric Vehiclesmentioning

confidence: 99%

The Potential Impacts of Electric Vehicles on Urban Air Quality in Shanghai City

Chen

et al. 2021

Sustainability

View full text Add to dashboard Cite

The Shanghai government has outlined plans for the new vehicles used for the public transportation, rental, sanitation, postal, and intra-city freight to be completely powered by electricity by 2020. This paper analyzed the characteristics of vehicle emissions in Shanghai in the past five years. The potential reduction in road traffic related emissions due to the promotion and application of electric vehicle in Shanghai was evaluated. The potential reduction was quantified by vehicular emissions. The vehicular emissions inventories are calculated by the COPERT IV model under the different scenarios, of which the results indicate that promoting electric vehicles is the efficient measure to control all road traffic related emissions and improve urban air quality. The results also provided basis and support for making policies to promote and manage electric vehicles.

show abstract

“…[21] Due to the outstanding research progress in the area of the smart grid, and renewable energy, power electronics industries have gained a great interest. Energy storage systems (ESS) such as batteries and supercapacitors have an important role in ensuring continuous power supply for the most critical electric loads, This contributes greatly to the advantage in the rationalization of energy using modern electronic devices, which consumes less energy in operation, [22][23][24][25][26][27][28] The reader consists of a current transformer to measure the current value of the consumer current, The Arduino controller, the wireless communication panel, and the computer. The software of the model is located in the control panel and also in the computer connected to the controller, which is the graphical user interface, which is used to display the data for the home use of the Electrical power to know the value of consumption at any moment.…”

Section: This Work Is Licensed Under a Creative Commons Attribution 4mentioning

confidence: 99%

Untitled

2020

International Journal of Innovative Research in Electrical, Ele

View full text Add to dashboard Cite

There are several solutions exist to save energy consumption in residential, commercial and industrial buildings. This paper presents a new model device to monitor the consumption of energy consumed in homes to rationalize energy consumption using a wireless meter using Arduino controller. The controller calculates the actual energy consumption at any moment and sends it to the computer using Wi-Fi module. The energy is digitally measured using Arduino and displayed graphically. An energy saving plan is proposed to save energy in residential loads. The recorded results are filtered to rationalize the consumption of energy to the extent required. To evaluate the performance of the proposed system, an experimental setup is conducted on residential apartments and rationalizing the consumption of electric energy by reducing the load without prejudice to the normal state of loads. The rate of rationalization in electric power is reached 25% of the value of total consumption. Therefore, the goal of controlling consumption and rationalization is achieved accurately and at the lowest cost possible and using advanced electronic elements to eliminate the inaccuracy in reading the meter home.

show abstract

Power Management Strategy for Battery Electric Vehicles

Cited by 26 publications

References 24 publications

Artificial neural networks applied on induction motor drive for an electric vehicle propulsion system

Artificial neural networks applied on induction motor drive for an electric vehicle propulsion system

The Potential Impacts of Electric Vehicles on Urban Air Quality in Shanghai City

Untitled

Contact Info

Product

Resources

About