Optimal operation of a system of charging hubs and a fleet of shared autonomous electric vehicles

Ancillary services and electric vehicles: An overview from charging clusters and chargers technology perspectives

“…In the first, charging is complementary to other user needs, such as going to the supermarket, in the second charging drives the choice of the user needs. Furthermore, charging behaviors are reflected at different charging sites: (i) home [155,156] and public residential charging [157,158]; (ii) curbside [159][160][161] and semi-public charging [162,163]; (iii) workplace charging [164]; (iv) fleet charging [165]; (v) large semi-public charging [166]; (vi) fast(en route) charging [167,168]; (vii) special semi-public charging [169,170]; (viii) charging forecourts [171,172]; (ix) semi-private charging [173] and (x) charging hubs [174]. Although it is still quite early for the clusters to mature, the charging clusters derived from the review are in line with the clusters used in the Working Group 4 of the IEA GEF Global e-mobility program [175,176].…”

Idle vehicle relocation strategy through deep learning for shared autonomous electric vehicle system optimization

“…Lee et al considered the uncertainties in SAEV systems and introduced reliability-based design optimization to derive an optimal SAEV system design that minimizes the total cost while satisfying the target reliability of the customer's wait time [15]. Melendez et al proposed a cyber-physical system (CPS) comprising a large fleet of SAEVs and a set of charging hubs for optimal planning and real-time operations [28].…”

An Integrated Optimisation-Simulation Framework for Scalable Smart Charging and Relocation of Shared Autonomous Electric Vehicles

“…The impacts and integration of SAEVs with grid distribution networks were analysed in [36]. The work in [37] is also an extension of [15,32] to incorporate more realistic grid constraints and passenger behaviour. While the proposed model still relies on a MILP optimisation, they also developed a myopic version of the method to deal with larger problem sizes.…”