The effect of electric vehicles on urban noise maps

Campello-Vicente, Héctor; Peral-Orts, Ramón; Campillo-Davó, Nuria; Velasco-Sánchez, Emilio

doi:10.1016/j.apacoust.2016.09.018

Cited by 77 publications

(31 citation statements)

References 4 publications

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“…In this study, the discussion of the effects of vehicle relocation of SAV on energy consumption and emissions is deliberately kept on an abstract level. No assumptions on the source of vehicle propulsion for the SAV is made, thus also no assumption on the magnitude of energy consumption, fine dust matter in the form tyre debris (see Kole et al 2017), noise pollution (see Campello-Vicente et al 2017) or other emissions can be made. The impact of the relocation strategies is instead based on the total driven mileage as a proxy, which can be used as input to any traffic emission estimation model.…”

Section: Driven Mileagementioning

confidence: 99%

Relocating shared automated vehicles under parking constraints: assessing the impact of different strategies for on-street parking

et al. 2020

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With shared mobility services becoming increasingly popular and vehicle automation technology advancing fast, there is an increasing interest in analysing the impacts of large-scale deployment of shared automated vehicles. In this study, a large fleet of shared automated vehicles providing private rides to passengers is introduced to an agent-based simulation model based on the city of Amsterdam, the Netherlands. The fleet is dimensioned for a sufficient service efficiency during peak-hours, meaning that in off-peak hours a substantial share of vehicles is idle, requiring vehicle relocation strategies. This study assesses the performance of zonal pro-active relocation strategies for on-demand passenger transport under constrained curbside parking capacity: (1) demand-anticipation, (2) even supply dispersion and (3) balancing between demand and supply of vehicles. The strategies are analysed in regard to service efficiency (passenger waiting times, operational efficiency), service externalities (driven mileage, parking usage) and service equity (spatial distribution of externalities and service provision). All pro-active relocation strategies are outperformed by a naïve remain-at-drop off-location strategy in a scenario where curbside parking capacity is in abundance. The demand-anticipation heuristic leads to the highest average waiting times due to vehicle bunching at demand-hotspots which results in an uneven usage of parking facilities. The most favourable results in regard to service efficiency and equity are achieved with the heuristics balancing demand and supply, at the costs of higher driven mileage due to the relocation of idle vehicles. These results open up opportunities for municipalities to accompany the introduction of large fleets of shared automated vehicles with suitable curbside management strategies that mitigate undesired effects.

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Section: Driven Mileagementioning

confidence: 99%

Relocating shared automated vehicles under parking constraints: assessing the impact of different strategies for on-street parking

et al. 2020

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“…Positive market response to targeted support measures [13,15,[17][18][19][20] Zero tailpipe emissions [21][22][23] Low operational noise levels [24][25][26] Local air quality benefits [21,22] Low operational costs [10,22] No fossil fuel use [22,23] Improved driving experience [27,28] Positive public image [27,29] Weaknesses High sticker prices for EVs [10,13,19,21,22,30] Shorter range and long recharging time [19,21,30,31] Lack of sufficient charging infrastructure and harmonized standards [15,[21][22][23] Grid restrictions [22,23,32] High battery prices [12,21,22] Limited model choice and after sales support [25,33] Higher emissions in EV and power production [21,23] Lower range real life vs certified…”

Section: Strengthsmentioning

confidence: 99%

Will Electric Vehicles Be Killed (again) or Are They the Next Mobility Killer App?

2020

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Electric vehicles (EVs) have been around for more than a hundred years. Nevertheless, their deployment has not been a sustainable success up until now. Many scientists, engineers and policymakers argue that EVs are a promising, maybe even indispensable option to achieve ambitious decarbonization goals, if powered by electricity from renewable energy sources. At the moment, the EVs market is gaining a lot of momentum and we may be near the point of no return for a sustained mass market deployment of electric vehicles. Many papers exist that describe future prospects of EVs. In our commentary we try to provide a bigger picture view and look at market and societal aspects. We analyze why previous generations of EVs were not successful and how current electric vehicles could become a sustainable success. We perform a semi-quantitative Strengths, Weaknesses, Opportunities, Threats (SWOT) analysis and find that current electric vehicle designs are technologically on par with or better than conventional alternatives. Car buyers go electric when the economics make sense to them. We conclude that incentives are needed for electric vehicles until battery costs lower—as much as to allow EVs to become cheaper—from a total cost of ownership (TCO) perspective, than other alternatives. Other policy measures are needed to overcome remaining barriers, especially in supporting the setup and operation of publicly accessible recharging points to overcome range anxiety. EVs in isolation may not be the next mobility killer app. The real next mobility killer app may emerge as an autonomous shared EV in a world where the border between public and private transport will cease to exist. The findings of our commentary are relevant for scientists, policymakers and industry.

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“…The electromagnetic noise, which is unique to EVs, makes the subjective perception very irritating [1,2]. For example, drivers and passengers are often plagued by "howling" and "buzzing" sounds under certain EV operating conditions [3,4]. This largely contributes to the fact that the SQ in EVs has become a major concern for their drivers and passengers [5].…”

Section: Introductionmentioning

confidence: 99%

Sound Quality Estimation of Electric Vehicles Based on GA-BP Artificial Neural Networks

2020

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The sound quality (SQ) and sound perception assessments of electric vehicles (EVs) clearly differ from those of conventional internal combustion engine vehicles (ICEVs). Therefore, it is essential to describe and evaluate the SQ of EVs. To evaluate the SQ in EVs, it is necessary to organize evaluators for conducting subjective jury tests, which are time-consuming and labor-intensive. In addition, the evaluation results are subject to the evaluators themselves and other external interferences. With the advancement of machine learning and artificial neural networks (ANNs), this problem can be well solved. This paper outlines a model for SQ estimation in EVs based on a genetic algorithm-optimized back propagation artificial neural network (GA-BP ANN). Moreover, the correlation between the physical-psychoacoustical parameters and the subjective SQ estimations obtained from the jury tests was investigated in this study. It was found that the GA-BP ANN SQ model has many advantages in comparison with the multiple linear regression (MLR) model in terms of precision and generalization. In addition, this method is ready to be applied for rapidly evaluating the SQ in EVs without jury tests, and it can also be of high significance in dealing with the acoustical designs and improvements of EVs in the future.

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The effect of electric vehicles on urban noise maps

Cited by 77 publications

References 4 publications

Relocating shared automated vehicles under parking constraints: assessing the impact of different strategies for on-street parking

Relocating shared automated vehicles under parking constraints: assessing the impact of different strategies for on-street parking

Will Electric Vehicles Be Killed (again) or Are They the Next Mobility Killer App?

Sound Quality Estimation of Electric Vehicles Based on GA-BP Artificial Neural Networks

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