Barriers to the adoption of electric cars: Evidence from an Italian survey

Giansoldati, Marco; Monte, Adriana; Scorrano, Mariangela

doi:10.1016/j.enpol.2020.111812

Cited by 51 publications

(23 citation statements)

References 26 publications

(90 reference statements)

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“…To achieve the widespread adoption of EVs, it is essential to have sufficient public charging infrastructure (CI) available such that users are able to comfortably and quickly recharge their EVs [5][6][7]. Currently, EV drivers in Germany are billed for CI usage at a fixed price per kWh and sometimes additionally for parking time.…”

Section: Introductionmentioning

confidence: 99%

Predicting Electric Vehicle Charging Station Availability Using Ensemble Machine Learning

2021

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Electric vehicles may reduce greenhouse gas emissions from individual mobility. Due to the long charging times, accurate planning is necessary, for which the availability of charging infrastructure must be known. In this paper, we show how the occupation status of charging infrastructure can be predicted for the next day using machine learning models— Gradient Boosting Classifier and Random Forest Classifier. Since both are ensemble models, binary training data (occupied vs. available) can be used to provide a certainty measure for predictions. The prediction may be used to adapt prices in a high-load scenario, predict grid stress, or forecast available power for smart or bidirectional charging. The models were chosen based on an evaluation of 13 different, typically used machine learning models. We show that it is necessary to know past charging station usage in order to predict future usage. Other features such as traffic density or weather have a limited effect. We show that a Gradient Boosting Classifier achieves 94.8% accuracy and a Matthews correlation coefficient of 0.838, making ensemble models a suitable tool. We further demonstrate how a model trained on binary data can perform non-binary predictions to give predictions in the categories “low likelihood” to “high likelihood”.

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Section: Introductionmentioning

confidence: 99%

Predicting Electric Vehicle Charging Station Availability Using Ensemble Machine Learning

2021

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“…To address this problem, Lei Gan et al [11] studied charging behavior of EV owners and designed a corresponding price incentive method to realize load transfer through price guidance. However, user behavior is an individual characteristic, and its sample data is difficult to collect [12,13], its typicality is questionable, and it is not universal [14]. Therefore, we transfer the study of user behavior to EVA.…”

Section: Introductionmentioning

confidence: 99%

Strategy of Large-Scale Electric Vehicles Absorbing Renewable Energy Abandoned Electricity Based on Master-Slave Game

Liu

Wang

et al. 2021

IEEE Access

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The rapid development of renewable energy power has improved global energy and environmental problems. However, with the high volatility of renewable energy, it is an important challenge to guarantee the consumption of renewable energy and the reliable operation of high percentage renewable energy power systems. To solve this problem, this paper proposes a tracking absorption strategy for renewable energy based on the interaction between the supply side and the demand side, which adjusts the charging process of electric vehicles (EVs) through electric vehicle aggregator (EVA) to realize the tracking absorption of renewable energy abandoned electricity. In view of this process, we analyze the interaction among power grid, EVA and renewable energy generation (REG) as well as their market characteristics. The master-slave game model of EVA and REG was constructed considering the charging behavior characteristics of EVs and the output characteristics of REGs. Then the model solving strategy based on soft actor-critic (SAC) algorithm is proposed, and the REG pricing strategy and EVA scheduling strategy are calculated to optimize the mutual benefits. The case analysis shows that, under the same scale of electric vehicles, the proposed method can promote about 93.89% of the power abandonment consumption of wind power system, 96.00% of the photovoltaic system, and 97.41% of the wind-solar system. This strategy reduces the electricity purchase cost of EVA, promotes the interaction among renewable energy , vehicles and power grid, and improves the utilization efficiency of renewable energy.

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“…One of the most important issues nowadays is to reduce environmental pollution by decreasing in car exhaust fumes. The transport sector is responsible for 24% of global CO 2 emissions (Bobeth & Kastner, 2020;Giansoldati, Monte, & Scorrano, 2020;Gómez Vilchez, Jochem, & Fichtner, 2020). The increase of environmental pollution awareness has mobilized car manufacturers to replace combustion engines -petrol and diesel engines with new promising technologies -hybrid and electric cars which draw energy from a battery, powered with renewable energy sources.…”

Section: Introductionmentioning

confidence: 99%

“…Depending on the country, the charging infrastructure is in a different development stage, and this is one of the most important aspects of electric cars' user-friendliness. Another issue is the charging time, which is usually too long for users and is considered a barrier to accepting electric vehicles (Bobeth & Kastner, 2020;Giansoldati et al, 2020;Gómez Vilchez et al, 2020;Milev et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Smart Payment Terminal in energy payment for electric and hybrid cars

Dankiewicz¹,

Hernes

Walaszczyk

et al. 2020

Informatyka Ekonomiczna

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One of the most important issues nowadays is reducing environmental pollution by decreasing car exhaust fumes. Increasing the customization of services forces companies to improve the selling process. One of the innovations used worldwide is the touchscreen in selfservice technology. The charging points for electric cars are often located in petrol stations so that the attendant carries out the payment process. However, there are more and more charging stations in other places, e.g. shopping centres, service stations, on-street parking, where there is no attendant. In such cases charging a car requires a mobile phone app for payment, and the app must be prepaid or have a connection to the customer's bank account. There is no possibility to pay in cash or to receive an invoice.The aim of this paper is to present the Smart Payment Terminal (SPT) as an innovation in the energy-selling process for electric and hybrid cars in places with no attendant, and show its features and advantages. The paper has a R&D character. The main contribution is developing an innovative Smart Payment Terminal that allows payment with cash, credit cards, and fleet cards, and may print a receipt or an invoice directly after the purchase.

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Barriers to the adoption of electric cars: Evidence from an Italian survey

Cited by 51 publications

References 26 publications

Predicting Electric Vehicle Charging Station Availability Using Ensemble Machine Learning

Predicting Electric Vehicle Charging Station Availability Using Ensemble Machine Learning

Strategy of Large-Scale Electric Vehicles Absorbing Renewable Energy Abandoned Electricity Based on Master-Slave Game

Smart Payment Terminal in energy payment for electric and hybrid cars

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