Comparing total cost of ownership of battery electric vehicles and internal combustion engine vehicles

Liu, Zhe; Song, Juhyun; Kubal, Joseph; Susarla, Naresh; Knehr, Kevin W.; Islam, Ehsan; Nelson, P.A.; Ahmed, Shabbir

doi:10.1016/j.enpol.2021.112564

Cited by 114 publications

(52 citation statements)

References 21 publications

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“…As mentioned earlier, the illustrated model was deployed on the Arduino kit for physical realization. The Arduino kit supplies the circuit by 5 volts, the LCD is supplied by 10 volts total, and the data wires are connected to (8,9,10,11,12,13) digital pins with (RS, E, D4, D5, D6, D7). LEDA is connected to the positive side to provide better vision, and the batteries are connected to a potentiometer to vary the output voltage controlling the SOC, as shown in Figure 4.…”

Section: Resultsmentioning

confidence: 99%

“…Accordingly, queuing delay and charging cost problems appear, and many researchers tried to solve them using different optimization techniques. Previous works were investigated to reduce the charging cost for EVs, either by introducing new charg-Volume 13, Number 6, 2022 ing schemes, integrating renewable energy sources, or utilizing decentralized in-door charging [6][7][8][9][10]. The current study focuses more on the other problem, queuing delay.…”

Section: Introductionmentioning

confidence: 99%

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Prototyping Design and Optimization of Smart Electric Vehicles/Stations System using ANN

Elkasrawy

Hassan

Abdellatif

et al. 2022

Int. j. electr. comput. eng. syst. (Online)

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This paper demonstrates an experimental attempt to prototype electric vehicle charging station’s (EVCS) decision-making unit, using artificial neural network (ANN) algorithm. The algorithm acts to minimize the queuing delay in the station, with respect to the vehicle state of charge (SOC), and the expected arrival time. A simplified circuit model has been used to prototype the proposed algorithm, to minimize the overall queuing delay. Herein, the worst-case scenario is considered by having number of electric vehicles arriving to the station at the same time greater than the charging points available in the station side. Accordingly, the optimization technique was applied to reduce the mean charging time of the vehicles and minimize queuing delay. Results showed that this model can help in reducing the queuing delay by around 20% of the mean charging time of the station, while referring to a bare model without ANN algorithm as a reference.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Prototyping Design and Optimization of Smart Electric Vehicles/Stations System using ANN

Elkasrawy

Hassan

Abdellatif

et al. 2022

Int. j. electr. comput. eng. syst. (Online)

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“…In terms of maintenance, refs. [93,94] discussed the gap between the operation cost for ICE and EV. In general, the purchasing cost for EV is higher due to the high battery cost, while the cumulative maintenance cost for ICE will be greater over time due to the frequent maintenance requirement for its powertrain.…”

Section: Key Areas For Future Research Workmentioning

confidence: 99%

Review of the Methods to Optimize Power Flow in Electric Vehicle Powertrains for Efficiency and Driving Performance

et al. 2022

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Electric vehicles (EV) are quickly gaining a foothold in global markets due to their zero tailpipe emissions and increasing practicality in terms of battery technologies. However, even though EV powertrains emit zero emissions during driving, their efficiency has not been fully optimized, particularly due the commonly used single-speed transmission. Hence, this paper provides an extensive review on the latest works carried out to optimize the power flow in EV powertrains using multispeed discrete transmission, continuously variable transmission and multi-motor configurations. The relevant literatures were shortlisted using a keyword search related to EV powertrain in the ScienceDirect and Scopus databases. The review focused on the related literatures published from 2018 onwards. The publications were reviewed in terms of the methodologies applied to optimize the powertrain for efficiency and driving performance. Next, the significant findings from these literatures were discussed and compared. Finally, based on the review, several future key research areas in EV powertrain efficiency and performance are highlighted.

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“…That poses a substantial problem. Today, the range issue continues to be a challenge for electric vehicles and limits the spread of these vehicles [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Effect of wind turbine designed for electric vehicles on aerodynamics and energy performance of the vehicle

Yıldız

Dandil

et al. 2022

THERM SCI

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This study aims to generate, independently from the electric network, one part of the electrical energy required in the existing electric vehicles, utilizing the wind en?ergy raised by on-the-go vehicles and thus enhancing the distance covered at one single charge. Regarding that aim, the effect of vehicle type wind turbine, which was designed so as not to cause an increase in the vehicle projection area, on the aerodynamic performance and energy efficiency of the vehicle was analyzed numer?ically. Using the shear stress transport k-? turbulence model, CFD simulations were conducted to determine the drag coefficients, pressure contours and velocity vectors of the designed basic vehicle model (M0) and its two different modified versions (M1, M2). The ANSYS-FLUENT software was used for numerical simulations. In the data obtained from the simulation results, the drag coefficient, compared to the M0 model, was determined to undergo an increase by 8.49% and 4.05%, respectively for M1 and M2 models. The total energy loss of the M2 model increased by 2.47% compared to the M0 model. The net energy gain produced through the wind turbine in the M2 model constituted approximately 5.13% of the total lost energy of the M0 model vehicle. In this context, the energy gain yielded from the wind turbine placed on the vehicle was observed to be higher than the wind turbine-caused energy loss. Thus, it was determined that the study positively contributed to the prolongation of the vehicle driving distance on a single charge.

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Comparing total cost of ownership of battery electric vehicles and internal combustion engine vehicles

Cited by 114 publications

References 21 publications

Prototyping Design and Optimization of Smart Electric Vehicles/Stations System using ANN

Prototyping Design and Optimization of Smart Electric Vehicles/Stations System using ANN

Review of the Methods to Optimize Power Flow in Electric Vehicle Powertrains for Efficiency and Driving Performance

Effect of wind turbine designed for electric vehicles on aerodynamics and energy performance of the vehicle

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