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

• 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.

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.