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EN
In electric vehicles, as in hybrids vehicles, a very important factor affecting the energy efficiency of the power-train is the ability to use the regenerative braking energy. Depending on the settings available in electric vehicles, the driver can choose different modes of operation: switch off the regenerative braking mode altogether, select the intensity of regenerative braking, or leave the control system in automatic mode. The last mode is often the only one available on eclectic vehicles, so the driver cannot decide whether to switch off or increase intensity of the regenerative braking. This paper presents a new method for evaluating the energy efficiency of electric vehicle powertrains under urban operating conditions. The presented method uses a procedure for mapping the operating conditions allowing to determine the reference level of energy consumption in relation to those recorded during the identification tests. Identification tests were carried out in the Tri-City area using electric vehicles of different purposes and operating parameters. Performed tests allowed to evaluate the regenerative braking efficiency of tested vehicle, which varies over a relatively wide range, for vehicle A from 33 to 77%, for vehicle B from 27 to 55% and for vehicle C from 36 to 58%. It can be concluded that one of the main factors determining the regenerative braking efficiency is the level of state of charge of the accumulator and the management algorithm used by the vehicle for controlling this parameter.
EN
In electric and hybrid vehicles, it is possible to recover energy from the braking process and reuse it to drive the vehicle using the batteries installed on-board. In the conditions of city traffic, the energy dissipated in the braking process constitutes a very large share of the total resistance to vehicle motion. Efficient use of the energy from the braking process enables a significant reduction of fuel and electricity consumption for hybrid and electric vehicles, respectively. This document presents an original method used to estimate the efficiency of the regenerative braking process for real traffic conditions. In the method, the potential amount of energy available in the braking process was determined on the basis of recorded real traffic conditions of the analysed vehicle. The balance of energy entering and leaving the battery was determined using the on-board electric energy flow recorder. Based on the adopted model of the drive system, the efficiency of the regenerative braking process was determined. The paper presents the results of road tests of three electric vehicles, operated in the same traffic conditions, for whom the regenerative braking efficiency was determined in accordance with the proposed model. During the identi-fication of the operating conditions of the vehicles, a global positioning system (GPS) measuring system supported by the original method of phenomenological signal correction was used to reduce the error of the measured vehicle’s altitude. In the paper, the efficiency of the re-cuperation process was defined as the ratio of the accumulated energy to the energy available from the braking process and determined for the registered route of the tested vehicle. The obtained results allowed to determine the efficiency of the recuperation process for real traffic conditions. They show that the recuperation system efficiency achieves relatively low values for vehicle No. 1, just 21%, while the highest value was achieved for vehicle No. 3, 77%. Distribution of the results can be directly related to the power of electric motors and battery capacities of the analysed vehicles.
PL
W niniejszej pracy przedstawiono propozycję klasyfikacji warunków miejskich. Wyszczególniono warunki występujące na budowie w warunkach miejskich utrudniające realizację obiektu. Przeanalizowania każdy z omówionych czynników pod kątem istotności jego wpływu na harmonogram budowy, a co za tym idzie – możliwego opóźnienia wykonywanych prac lub pojawieniu się nieoczekiwanych kosztów w sytuacji, w której nie bierze się wymienionych wyżej czynników pod uwagę przy prognozowaniu czasu trwania budowy. Sporządzono dwa harmonogramy budowy, z których jeden uwzględniał wpływ najbardziej istotnych czynników utrudniających realizację, a występujących w warunkach miejskich. Porównanie długości ścieżki krytycznej wyżej wspomnianych harmonogramów pozwoliło odpowiedzieć na pytanie, które z analizowanych czynników utrudniających realizację – a występujących w warunkach miejskich – wpływa najbardziej na wydłużenie czasu trwania budowy. Celowym jest podjęcie dalszych badań – na innych placach budów – w celu weryfikacji tez przedstawionych w niniejszej pracy.
EN
The urban conditions were defined in this paper as a downtown location of the building site. The negative condition appearing there were listed. Each factor (arisen from urban condition) influencing the time schedule was analyzed taking into account the strength of its influence on a time schedule. The basis for analysis was a real building site in Warsaw where office building is erecting. Concrete works has been chosen for analysis in this paper. Two time schedules were prepared. One for the building erecting within ideal surrounding. The other one for the building located – as the example office building – close to the blocks of flats and with the very limited area for the backyard. The lengths of critical paths were calculated and compared. This analysis has allowed for evaluation which of urban circumstances has significant, negative influence on time schedule basing on given project. It is recommended to carry more research in order to confirm findings comprised in this paper.
EN
The influence of air pollutants on visibility in Warsaw Agglomeration has been investigated. Following pollutants were considered: PM10, SO2, NO2 and O3, while meteorological parameters included: air temperatures (mean, minimum, maximum), solar radiation, relative air humidity, rainfall rates and wind speed. Initial analyses were performed with the use of principal component analysis (PCA). In next stages, the logistic regression (LR), the analysis of variance (ANOVA), one-way classification and a model path of generalized regression models (GRM) were applied. PCA analysis showed that in the cold season the visibility index depends on PM10, SO2, NO2 and the temperatures: T, Tmin, and Tmax. In the warm season, the index of visibility is mostly shaped by four elements: O3, T, Tmax and solar radiation. Logistic regression model indicated that in the warm season only two variables are significantly related to visibility: PM10 and relative humidity of air. Regularities in the cold season shown by the LR correspond with the conclusions from the PCA. Among meteorological conditions, the most important is air temperature, but only Tmax preserves the same direction of influence as the one pointed by the PCA model.
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