Journal article
Inhomogeneous Markov Models for Describing Driving Patterns
It has been predicted that electric vehicles will play a crucial role in incorporating a large renewable component in the energy sector. If electric vehicles are integrated in a naive way, they may exacerbate issues related to peak demand and transmission capacity limits while not reducing polluting emissions.
Optimizing the charging of electric vehicles is paramount for their successful integration. This paper presents a model to describe the driving patterns of electric vehicles in order to provide primary input information to any mathematical programming model for optimal charging. Specifically, an inhomogeneous Markov model that captures the diurnal variation in the use of a vehicle is presented.
The model is defined by the time-varying probabilities of starting and ending a trip, and is justified due to the uncertainty associated with the use of the vehicle. The model is fitted to data collected from the actual utilization of a vehicle. Inhomogeneous Markov models imply a large number of parameters.
The number of parameters in the proposed model is reduced using B-splines.
| Language: | English |
|---|---|
| Publisher: | IEEE |
| Year: | 2017 |
| Pages: | 581-588 |
| ISSN: | 19493061 and 19493053 |
| Types: | Journal article |
| DOI: | 10.1109/TSG.2016.2520661 |
| ORCIDs: | Møller, Jan K. and Madsen, Henrik |
B-splines SDG 7 - Affordable and Clean Energy driving patterns electric vehicles hidden Markov model inhomogeneous Markov chain
B-spline Data models Electric vehicles Hidden Markov models Markov processes Nonhomogeneous media Numerical models diurnal variation electric vehicle charging electric vehicle driving patterns inhomogeneous Markov model mathematical programming mathematical programming model splines (mathematics) time-varying probability
