Review on Contactless Power Transfer for Electric Vehicle Charging

Ravikiran, Vaka; Keshri, Ritesh Kumar

doi:10.3390/en10050636

Cited by 54 publications

(27 citation statements)

References 69 publications

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“…In the same direction, the Charging While Driving (CWD) system is another technology designed to reduce the time spent charging. This contactless-charging technology allows charging the battery with inductors installed in the roads [17]. Again, these technologies, once deployed on enough vehicles and road infrastructures, would massively contribute to mitigating range anxiety.…”

Section: Range-anxiety Mitigation Methodsmentioning

confidence: 99%

Influence of Adaptive Human–Machine Interface on Electric-Vehicle Range-Anxiety Mitigation

Musabini

Nguyen

Rouyer³

et al. 2020

MTI

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The electrification of vehicles is without a doubt one of the milestones of today’s automotive technology. Even though industry actors perceive it as a future standard, acceptance, and adoption of this kind of vehicles by the end user remain a huge challenge. One of the main issues is the range anxiety related to the electric vehicle’s remaining battery level. In the scope of the H2020 ADAS&ME project, we designed and developed an intelligent Human Machine Interface (HMI) to ease acceptance of Electric Vehicle (EV) technology. This HMI is mounted on a fake autonomous vehicle piloted by a hidden joystick (called Wizard of Oz (WoZ) driving). We examined 22 inexperienced EV drivers during a one-hour driving task tailored to generate range anxiety. According to our protocol, once the remaining battery level started to become critical after manual driving, the HMI proposed accurate coping techniques to inform the drivers how to reduce the power consumption of the vehicle. In the following steps of the protocol, the vehicle was totally out of battery, and the drivers had to experience an emergency stop. The first result of this paper was that an intelligent HMI could reduce the range anxiety of the driver by proposing adapted coping strategies (i.e., transmitting how to save energy when the vehicle approaches a traffic light). The second result was that such an HMI and automated driving to a safe spot could reduce the stress of the driver when an emergency stop is necessary.

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Section: Range-anxiety Mitigation Methodsmentioning

confidence: 99%

Influence of Adaptive Human–Machine Interface on Electric-Vehicle Range-Anxiety Mitigation

Musabini

Nguyen

Rouyer³

et al. 2020

MTI

View full text Add to dashboard Cite

show abstract

“…IPT systems could be operated in static, car parks mode [51][52][53][54][55][56][57] or when running [49,[58][59][60][61][62][63]. The dynamic mode, yet more complex in its functioning control and the necessity for particular infrastructures, presents the opportunity of beating the obstacles correspond to the weighty battery storage aboard, the lengthy charging period and the restricted autonomy in the static mode.…”

Section: Inductive Power Transfer Ipt In Ev and Hybrid Evmentioning

confidence: 99%

Review of Contactless Energy Transfer Concept Applied to Inductive Power Transfer Systems in Electric Vehicles

Razek

2021

Applied Sciences

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Nowadays the groundbreaking tools of contactless energy transfer reveals new opportunities to supply portable devices with electrical energy by eliminating cables and connectors. One of the important applications of such technology is the energy providing to electric and hybrid vehicles, (EV) and (HEV). These contribute to the use of cleaner energy to protect our environment. In the present paper, after exposing the available contactless energy transfer (CET) available systems, we examine the appropriateness of these systems for EV. After such exploration, it is shown that the most suitable solution is the inductive power transfer (IPT) issue. We analyze such procedure in general and indicate its main usages. Next, we consider the practice of IPT in EV and the different option in the energy managing in EV and HEV concerning battery charging. Following, we review the modes of using the IPT in immobile case and in on-road running. Following, the modeling issues for the IPT system escorting the vehicle structure are then exposed. Lastly, the electromagnetic compatibility (EMC) and human exposure analyses are assessed involving typical appliance.

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“…In the process of the AC charging of electric vehicles, the charging efficiency of the electric vehicle will be determined by the on-board charger, the internal resistance of the battery and the battery temperature [31]. The higher the efficiency of the car charger, the lower the internal resistance of the battery and the more reasonable the battery temperature, the higher the charging efficiency of the electric vehicle will be [31]. To simplify the calculation, this paper uses the average charging efficiency.…”

Section: Charging Efficiency Of Electric Vehicles (η 2 )mentioning

confidence: 99%

Prediction and Analysis of the Relationship between Energy Mix Structure and Electric Vehicles Holdings Based on Carbon Emission Reduction Constraint: A Case in the Beijing-Tianjin-Hebei Region, China

Zhao

Fan

2019

Sustainability

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In response to air pollution problems caused by carbon emissions, electric vehicles are widely promoted in China. Since thermal power generation is the main form of power generation, the large-scale development of electric vehicles is equivalent to replacing oil with coal, which will accordingly result in carbon emissions increasing if the scale of electric vehicles exceeds a certain limit. A relationship model between regional energy mix structure and electric vehicles holdings under the constraint of carbon emission reduction is established to perform a quantitative analysis of the limitation mechanism. In order to measure the scale of the future electric vehicle market under the constraint of carbon emissions reduction, a method called Extreme Learning Machine optimized by Improved Particle Swarm Optimization (IPSO-ELM) with higher precision than Extreme Learning Machine (ELM) is proposed to predict the power structure and the trend of electric vehicle development in the Beijing-Tianjin-Hebei region from 2019–2030. The calculation results show that the maximum number of electric vehicles must not exceed 19,340,000 and 26,867,171 based on emissions reduction aims and also the predicted energy mix structure in the Beijing-Tianjin-Hebei region in 2020 and 2030. At this time, the ratio of electric vehicles to traditional car ownership is 75.6% and 78.3%. The proportion of clean energy generation should reach 0.314 and 0.323 to match a complete replacement of traditional fuel vehicles for electric vehicles. A substantial increase in clean energy generation is needed so that the large-scale promotion of electric vehicles can still achieve the goal of carbon reduction. Therefore, this article will be helpful for policy-making on electric vehicle development scale and energy mix structure in the Beijing-Tianjin-Hebei region.

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Review on Contactless Power Transfer for Electric Vehicle Charging

Cited by 54 publications

References 69 publications

Influence of Adaptive Human–Machine Interface on Electric-Vehicle Range-Anxiety Mitigation

Influence of Adaptive Human–Machine Interface on Electric-Vehicle Range-Anxiety Mitigation

Review of Contactless Energy Transfer Concept Applied to Inductive Power Transfer Systems in Electric Vehicles

Prediction and Analysis of the Relationship between Energy Mix Structure and Electric Vehicles Holdings Based on Carbon Emission Reduction Constraint: A Case in the Beijing-Tianjin-Hebei Region, China

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