A novel model of electric vehicle fleet aggregate battery for energy planning studies

Branimir Škugor and Joško Deur

Energy, 2015, vol. 92, issue P3, 444-455

Abstract: The paper proposes an aggregate battery modelling approach for an (electric vehicle) EV fleet, which is aimed for energy planning studies of EV-grid integration. The proposed model improves on the existing, basic aggregate battery modelling approach by accounting for a variable structure of the aggregate battery systems, variable (state of charge) SoC constraints and specific input time-distributions such as those of average SoC at destination and number of arriving and departing vehicles. In the particular case-study presented, the input distributions are reconstructed from a large set of delivery vehicle fleet driving missions, including simulation of individual vehicle behaviours over the full set of driving cycles. The charging power input is obtained by using a dynamic programming-based optimisation algorithm aimed at finding a global optimum in terms of minimised electricity cost. For the purpose of proposed model validation and its comparison with the basic model, a distributed fleet vehicle model is developed, where a specific algorithm is proposed for distributing the optimised charging power input to charging inputs of individual vehicles.

Keywords: Electric vehicles; Fleet; Aggregate battery; Modelling; Optimisation; Energy planning (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544215006118
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:92:y:2015:i:p3:p:444-455

DOI: 10.1016/j.energy.2015.05.030

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().