Variability of daily car usage and the frequency of long-distance driving

Plötz, Patrick; Sprei, Frances

doi:10.1016/j.trd.2021.103126

Cited by 3 publications

(2 citation statements)

References 42 publications

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“…Yet, the vehicles in the original definition of WLTP seem to include some long-distance driving as the UF curve would otherwise reach 100% already for small ranges. Please note that the share of long-distance driving in annual vehicle km travelled increases with the observation period of vehicles [42,43]. The mean WLTP range in our data is about 55 km resulting in 77% WLTP UF.…”

Section: Deviation Between Type Approval and Actual Phev Fuel Consump...mentioning

confidence: 63%

Factors impacting real-world fuel economy of plug-in hybrid electric vehicles in Europe – an empirical analysis

Mandev,

Plötz,

Sprei

2024

Environ. Res. Commun.

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Plug-in hybrid electric vehicles (PHEVs) combine an electric motor with an internal combustion engine and can reduce greenhouse gas emissions from transport if mainly driven on electricity. The environmental benefit of PHEVs strongly depends on its usage and charging behavior. Several studies have demonstrated low electric driving shares (EDS) of many PHEVs. However, there is limited evidence on which vehicle properties affect the EDS of PHEVs to which extent. Here, we provide an empirical and quantitative analysis of real-world EDS and fuel consumption and look at how they are impacted by factors related to vehicle properties such as range, system power and mass. We complement previous studies on real-world EDS and fuel consumption of PHEVs by combining two different data sets, with almost 100,000 vehicles in total, over 150 models in 41 countries, which is combined the largest PHEV sample in Europe to date to be analyzed in the literature. We find that an increase of 10 km of type approval range leads on average to 13 – 17 % fuel consumption decrease and 1 – 4 % EDS increase. Furthermore, a 1 kW increase in system power per 100 kg of vehicle mass is associated with an average increase of 7 – 9 % in fuel consumption and a decrease of up to 2% in EDS. We also find that long-distance driving and charging behavior are the largest non-technical factors for the deviation between type-approval and real-world data. Furthermore, PHEV fuel consumption and related tail-pipe emissions in Europe are on average higher than official EU values.

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Section: Deviation Between Type Approval and Actual Phev Fuel Consump...mentioning

confidence: 63%

Factors impacting real-world fuel economy of plug-in hybrid electric vehicles in Europe – an empirical analysis

Mandev,

Plötz,

Sprei

2024

Environ. Res. Commun.

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“…Although the daily vehicle kilometre travelled (VKT) vary from day to day [10], the driven distance was shown to be less than 100 km more than 80% of the days in Italy [11]. In Europe and North America, the driven distance is less than 200 km 97% of the days [12].…”

Section: Introductionmentioning

confidence: 99%

From PV to EV: Mapping the Potential for Electric Vehicle Charging with Solar Energy in Europe

Jeannin,

Pena-Bello,

Dumoulin

et al. 2024

IJSEPM

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The electrification of the transport sector is a key element in decarbonizing our societies. However, energy systems will have to cope with additional electricity demand due to the charging needs of electric vehicles (EVs) together with the integration of fluctuating renewable energy sources. Shifting EV charging during the day can contribute to absorbing photovoltaic (PV) production peaks and limiting the additional demand during peak periods. The EV batteries can then be discharged to the grid or home during the evening or other demand peaks. We developed a new methodology to quantify the flexibility gained from the EV-PV coupling from local mobility habits and local (or decentralized) photovoltaic production. Our approach first focuses on the geospatial modelling of mobility habits across Europe to quantify energy demand for charging. The charging demand is then distributed between residential areas, workplaces and points of interest (shopping, leisure, etc.) to model the spatiotemporal distribution of energy needs. We show and discuss a practical case in Copenhagen illustrating the impact of charging behaviour for three different scenarios. The methodology is implemented as a calculation module in the open-source online geographic tool for energy transition planning, Citiwatts.

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