Dynamic Programming-based Optimisation of Charging an Electric Vehicle Fleet System Represented by an Aggregate Battery Model

This paper proposes a dynamic programming-based (DP) optimisation method of charging an electric vehicle (EV) fleet modelled as a single, so-called aggregate battery. The main advantage of the approach is that it provides a globally optimal solution, with a relatively non-excessive computational load owing to a low order of the aggregate battery model. The method is illustrated through a case study of an isolated, hypothetically electrified delivery truck transport system charged from both grid and renewable energy sources (RES). Two scenarios of energy production from RES (with and without excess in RES production), along with several electricity price models are studied. The DP optimisation results are compared with the results obtained by an existing heuristic charging algorithm used in EnergyPLAN software to illustrate the DP algorithm advantages in minimising the charging energy cost and satisfying the aggregate battery charge sustaining conditions. The proposed DP optimisation method can be used in various energy planning studies, as well as a core of the supervisory/aggregator level of hierarchical EV fleet charging strategies.