Optimal Charging of Plug-In Electric Vehicle: Considering Travel Behavior Uncertainties and Battery Degradation

Abstract

The negative environmental impacts of using fossil fuel-powered vehicles underlined the need for inventing an alternative eco-friendly transportation fleet. Plug-in electrical vehicles (PEVs) are introduced to cut the continuing increase in energy use and carbon emission of the urban mobility. However, the increased demand for mobility, and therefore energy, can create constraints on the power network which can reduce the benefits of electrification as a certain and reliable source. Thus, the rise in the use of electric vehicles needs electric grids to be able to feed the increased energy demand while the current infrastructure supports it. In this paper, we introduce a methodological framework for scheduling smart PEVs charging by considering the uncertainties and battery degradation. This framework includes an economic model for charging and discharging of PEVs which has been implemented in a 21-node sample distribution network with a wind turbine as a distributed generation (DG) unit. Our proposed approach indicates that the optimal charging of the PEVs has a high impact on the distribution network operation, particularly under the high market penetration of PEVs. Thus, the smart grid to vehicle (G2V) charging mode is a potential solution to maximize the PEV’s owner profit, while considering the battery degradation cost of the PEVs. The simulation result indicates that smart charging effectuation is economical.

Integrating Battery Aging in the Optimization for Bidirectional Charging of Electric Vehicles

“…On average, Mode III yields a 7.8% lower operating cost compared with Mode I; similar results can be found in the literature, e.g. 5.4% in [8] and 13.2% with simplifications in [15]. Although p < 0 in (14b), i.e.…”

“…This requires adequate representation of EV operating cost, and/or inclusion of user comfort [20]. B) Battery aging is a crucial factor for SC economics that may not be neglected [6]- [8], [24]. In addition, EV users desire transparent EV operating cost, including costs inherent in battery aging [21].…”

“…Calculating the evolution of e max requires a battery aging model as described in the following. 8 Charging and discharging causes the battery's anode and cathode to decay (cyclic aging). Among other processes, a loss of active lithium material occurs due to mechanical stress, see also [33]; For the battery cells used in this study, the cyclic aging increment…”

Systematic Literature Review on Smart Mobility: A Framework for Future “Quantitative” Developments

“…However, the increased demand for mobility, and therefore energy, can create constraints on the power network, which can reduce the benefits of electrification as a certain and reliable source. Thus, the rise in the use of electric vehicles needs electric grids to be able to feed the increased energy demand while the current infrastructure supports it (Tayarani et al 2019). For these reasons, some studies have deepened the electricity balancing process applied to the electric bus grid (Wu et al 2019).…”

Coordinated optimisation of PEV charging with the support of reactive discharging and phase switching in unbalanced active distribution networks

“…In [10, 11], dynamic pricing schemes were built to prompt users to charge PEVs when electricity prices are low. The authors in [12–14] proposed coordinated PEV charging strategies to minimise the charging cost and maximise the users’ benefits. In [15], the charging behaviour of PEV users was studied first and then an energy management method was designed to reduce the energy cost.…”