Mode-decomposing Analysis of the Extreme Load in Hybrid Electric Vehicles Using Extreme Value Theory

In a hybrid electric vehicle (HEV), the hybrid system, which is equipped with an engine and a motor, is a key component. However, given the multimode characteristics of HEV, the original extreme load of the engine or motor is not independent and the random variables cannot be directly fitted by the extreme value theory (EVT). Thus, this paper proposes a mode-decomposing application method (MDAM) using EVT. Based on the method, three typical distributions, including the Fréchet distribution, the Gumbel distribution, and the Weibull distribution, were combined as a unified expression, and it was adopted to fit the extreme loads within different modes of HEV. By comparing the fitting results, especially the shapes of the curves, the distributions of the load under different modes vary from each other, so the feasibility and necessity of MDAM in HEV are proved, and a new thought for fitting the extreme load in HEV is provided, which will contribute to improve the fitting accuracy.

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Maximum Values of Frozen Soil’s Depth and Hoar Frost’s Thickness Estimated on the Basis of Extreme Values Theory

Present Environment and Sustainable Development ◽

10.2478/pesd-2018-0027 ◽

2018 ◽

Vol 12 (2) ◽

pp. 13-23

Author(s):

Maria Nedealcov ◽

Valentin Răileanu ◽

Gheorghe Croitoru ◽

Cojocari Rodica ◽

Crivova Olga

Keyword(s):

Risk Factors ◽

Extreme Value Theory ◽

Extreme Values ◽

Gumbel Distribution ◽

Value Theory ◽

Extreme Value ◽

Frozen Soil ◽

Return Interval ◽

The Mean ◽

Extreme Values Theory

Abstract Extreme climatic phenomena present risk factors for agriculture, health, constructions, etc. and are studied profoundly these past years using extreme value theory. Several relation that describe positive extreme values’ probability Generalized Extreme Value and Gumbel distribution are presented in the article. As a example, we show the maps of characteristic and reference values of the maximum depth of the frozen soil and thickness of hoar-frost with a probability of exceeding per year equal to 0,02, which is equivalent to the mean return interval of 50 years. The obtained results could serve as a base for elaboration of national annexes in constructions.

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The analysis of design wind speed estimates specified in the National Building Code of Canada

Canadian Journal of Civil Engineering ◽

10.1139/l06-133 ◽

2007 ◽

Vol 34 (4) ◽

pp. 513-524 ◽

Cited By ~ 2

Author(s):

M D Pandey ◽

Y An

Keyword(s):

Wind Speed ◽

Order Statistics ◽

Extreme Value Theory ◽

Gumbel Distribution ◽

Value Theory ◽

Extreme Value ◽

Building Code ◽

Peaks Over Threshold ◽

Wind Speed Data ◽

Design Wind Speed

The design wind pressures specified in the 2005 National Building Code of Canada (NBCC) have been derived from the Gumbel distribution fitted to annual maximum wind speed data collected up to early 1990s. The statistical estimates of the annual maxima method are affected by a relatively large sampling variability, since the method considers a fairly small subset of available wind speed records. Advanced statistical methods have emerged in recent years with the purpose of reducing both sampling and model uncertainties associated with extreme quantile estimates. The two most notable methods are the peaks-over-threshold (POT) and annually r largest order statistics (r-LOS), which extend the data set by including additional maxima observed in wind speed time series. The objective of the paper is to explore the use of advanced extreme value theory for updating the design wind speed estimates specified in the Canadian building design code. The paper re-examines the NBCC method for design wind speed estimation and presents the analysis of the latest Canadian wind speed data by POT, r-LOS, and annual maxima methods. The paper concludes that r-LOS method is an effective alternative for the estimation of extreme design wind speed.Key words: wind speed, extreme value theory, order statistics, return period, maximum likelihood method, peaks-over-threshold method, generalized extreme value distribution, Gumbel distribution, generalized Pareto distribution.

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Outlier Detection in Adaptive Functional-Coefficient Autoregressive Models Based on Extreme Value Theory

Mathematical Problems in Engineering ◽

10.1155/2013/910828 ◽

2013 ◽

Vol 2013 ◽

pp. 1-9 ◽

Cited By ~ 2

Author(s):

Ping Chen ◽

Ling Dong ◽

Wanyi Chen ◽

Jin-Guan Lin

Keyword(s):

Extreme Value Theory ◽

Gumbel Distribution ◽

Value Theory ◽

Extreme Value ◽

Likelihood Ratio Tests ◽

Simulation Studies ◽

Test Statistics ◽

Significance Level ◽

Time Series Simulation ◽

Functional Coefficient

This paper proposes several test statistics to detect additive or innovative outliers in adaptive functional-coefficient autoregressive (AFAR) models based on extreme value theory and likelihood ratio tests. All the test statistics follow a tractable asymptotic Gumbel distribution. Also, we propose an asymptotic critical value on a fixed significance level and obtain an asymptoticp-value for testing, which is used to detect outliers in time series. Simulation studies indicate that the extreme value method for detecting outliers in AFAR models is effective both for AO and IO, for a lone outlier and multiple outliers, and for separate outliers and outlier patches. Furthermore, it is shown that our procedure can reduce possible effects of masking and swamping.

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