Modeling total lifecycle ownership cost of battery electric vehicle based on urban traffic data in China

AbstractThis paper describes the CO2 emissions of the additional electricity generation needed in Germany for battery electric vehicles. Different scenarios drawn up by the transmission system operators in past and for future years for expansion of the energy sources of electricity generation in Germany are considered. From these expansion scenarios, hourly resolved real-time simulations of the different years are created. Based on the calculations, it can be shown that even in 2035, the carbon footprint of a battery electric vehicle at a consumption of 22.5 kWh/100 km including losses and provision will be around 100 g CO2/km. Furthermore, it is shown why the often-mentioned German energy mix is not suitable for calculating the emissions of a battery electric vehicle fleet. Since the carbon footprint of a BEV improves significantly over the years due to the progressive expansion of renewable-energy sources, a comparison is drawn at the end of this work between a BEV (29.8 tons of CO2), a conventional diesel vehicle (34.4 tons of CO2), and a diesel vehicle with R33 fuel (25.8 tons of CO2) over the entire useful life.

Download Full-text

Short-term forecast model of taxi demand based on time and space heterogeneity

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-210872 ◽

2021 ◽

pp. 1-12

Author(s):

Zhiyu Yan ◽

Shuang Lv

Keyword(s):

New York ◽

New York City ◽

York City ◽

Urban Traffic ◽

Percentage Error ◽

Density Level ◽

Traffic Data ◽

Short Term ◽

Time And Space ◽

Density Data

Accurate prediction of traffic flow is of great significance for alleviating urban traffic congestions. Most previous studies used historical traffic data, in which only one model or algorithm was adopted by the whole prediction space and the differences in various regions were ignored. In this context, based on time and space heterogeneity, a Classification and Regression Trees-K-Nearest Neighbor (CART-KNN) Hybrid Prediction model was proposed to predict short-term taxi demand. Firstly, a concentric partitioning method was applied to divide the test area into discrete small areas according to its boarding density level. Then the CART model was used to divide the dataset of each area according to its temporal characteristics, and KNN was established for each subset by using the corresponding boarding density data to estimate the parameters of the KNN model. Finally, the proposed method was tested on the New York City Taxi and Limousine Commission (TLC) data, and the traditional KNN model, backpropagation (BP) neural network, long-short term memory model (LSTM) were used to compare with the proposed CART-KNN model. The selected models were used to predict the demand for taxis in New York City, and the Kriging Interpolation was used to obtain all the regional predictions. From the results, it can be suggested that the proposed CART-KNN model performed better than other general models by showing smaller mean absolute percentage error (MAPE) and root mean square error (RMSE) value. The improvement of prediction accuracy of CART-KNN model is helpful to understand the regional demand pattern to partition the boarding density data from the time and space dimensions. The partition method can be extended into many models using traffic data.

Download Full-text

Recent progress in battery electric vehicle noise, vibration, and harshness

Science Progress ◽

10.1177/00368504211005224 ◽

2021 ◽

Vol 104 (1) ◽

pp. 003685042110052

Author(s):

Xia Hua ◽

Alan Thomas ◽

Kurt Shultis

Keyword(s):

Electric Vehicle ◽

Recent Progress ◽

Combustion Engine ◽

Automatic Transmission ◽

Battery Electric Vehicle ◽

Vehicle Noise ◽

Conventional Vehicle ◽

Transfer Case ◽

Air Intake ◽

Noise Vibration

As battery electric vehicle (BEV) market share grows so must our understanding of the noise, vibration, and harshness (NVH) phenomenon found inside the BEVs which makes this technological revolution possible. Similar to the conventional vehicle having encountered numerous NVH issues until today, BEV has to face many new and tough NVH issues. For example, conventional vehicles are powered by the internal combustion engine (ICE) which is the dominant noise source. The noises from other sources were generally masked by the combustion engine, thus the research focus was on the reduction of combustion engine while less attention was paid to noises from other sources. A BEV does not have ICE, automatic transmission, transfer case, fuel tank, air intake, or exhaust systems. In their place, there is more than enough space to accommodate the electric drive unit and battery pack. BEV is quieter without a combustion engine, however, the research on vehicle NVH is even more significant since the elimination of the combustion engine would expose many noise behaviors of BEV that were previously ignored but would now seem clearly audible and annoying. Researches have recently been conducted on the NVH of BEV mainly emphasis on the reduction of noise induced by powertrain, tire, wind and ancillary system and the improvement of sound quality. This review paper will focus on recent progress in BEV NVH research to advance the BEV systems in the future. It is a review for theoretical, computational, and experimental work conducted by both academia and industry in the past few years.

Download Full-text