A novel model of electric vehicle fleet aggregate battery for energy planning studies. (1st December 2015)

• Record Type: Journal Article
• Authors: Škugor, Branimir; Deur, Joško
• Is Part Of: Energy. Volume 92:Part 3(2015)
• Page Start: 444
• Page End: 455
• Publication Date: 2015-12-01
• Subjects: Electric vehicles -- Fleet -- Aggregate battery -- Modelling -- Optimisation -- Energy planning; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2015.05.030
• Languages: English
• ISSNs: 0360-5442

Abstract

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. Highlights: A novel aggregate battery model is proposed for an electric vehicle (EV) fleet. Dynamic programming is used for model-based EV fleet charging optimisation. An empirical algorithm distributes the optimised charging power to individual EVs. Validation results point to a favourable accuracy of the proposed model.