Review on Pulse Width Modulation and Optimization Techniques for Multilevel Inverters

Vedith, S.; Tej, J. N. Bhanu; Sampath, Swetha; Sree, M. Usha; Rao, Potu N.; Neelima, K.

doi:10.1007/978-981-16-8542-2_19

Cited by 2 publications

(1 citation statement)

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“…To do so, flows categorized as Class A and Class B, namely, 2-axle vehicles, were chosen. For allocation, the disaggregated traffic must also be considered [41][42][43][44] (highway concessionaire of the analyzed highway section), while the fuel sale figure is only available in the aggregate for the entire highway network. Therefore, Equation ( 4) is used for allocation.…”

Section: Energy Allocation To Sectionsmentioning

confidence: 99%

Innovative Energy Approach for Design and Sizing of Electric Vehicle Charging Infrastructure

Martini,

Aimar,

Borghetti

et al. 2024

Infrastructures

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In Italy, the availability of service areas (SAs) equipped with charging stations (CSs) for electric vehicles (EVs) on highways is limited in comparison to the total number of service areas. The scope of this work is to create a prototype and show a different approach to assessing the number of inlets required on highways. The proposed method estimates the energy requirements for the future electric fleet on highways. It is based on an energy conversion that starts with the fuel sold in the highway network and ends with the number of charging inlets. A proposed benchmark method estimates energy requirements for the electric fleet using consolidated values and statistics about refueling attitudes, with factors for range correction and winter conditions. The results depend on assumptions about future car distribution, with varying numbers of required inlets. The analysis revealed that vehicle traffic is a critical factor in determining the number of required charging inlets, with significant variance between different SAs. This study highlights the necessity of incorporating factors like weather, car charging power, and the future EV range into these estimations. The findings are useful for planning EV charging infrastructure, especially along major traffic routes and in urban areas with high-range vehicles relying on High-Power DC (HPDC) charging. The model’s applicability to urban scenarios can be improved by considering the proportion of energy recharged at the destination. A key limitation is the lack of detailed origin–destination (OD) highway data, leading to some uncertainty in the calculated range ratio coefficient and underscoring the need for future research to refine this model.

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Section: Energy Allocation To Sectionsmentioning

confidence: 99%