Finite State Machine Modelling for The Performance Analysis of An Integrated Road-Power Infrastructure with A Hybrid Fleet of EVs And ICVs

Probabilistic Scenario Analysis of Integrated Road-Power Infrastructures with Hybrid Fleets of Evs and Icvs

“…• ID of the p-th ICV p , and the r-th EV r This allows collecting trajectories (i.e., time series) of travelled nodes and states (S 0 , S 1 , S 2 , S 3 , S 4 , S 5 ) for each vehicle EV r and ICV p , traffic volume , SoC r (t), FL p (t), N CSx (t) or N GSξ (t), CH x (t) for each edge i,j, charging station CS x and gas station GS ξ . The availability of such multi-dimensional time series is the main benefit of the modelling framework proposed with respect to flow-vehicles models that lump all this information in integral flow-related measures that lack of details with respect to the state of the vehicles running on the road system [20]. ; when it passes by a charging station CS x or gas station GS ξ , recharge or refill is performed in case the SoC r (t) or FL p (t) equals the critical amount SoC critical or FL critical or FL critical− c that would not allow the vehicle to reach to the next charging station CS x+1 or gas station GS ξ+1 , or the vehicle travels to the node where an intersection is reached and where, depending on the driver turning rate parameter φ (here considered equal to 0.5), one of the other roads is selected; the travelling motion cycle continues until Destination (D) is reached.…”

“…The hourly traffic pattern related to the selected network is extracted from the Hourly Traffic Demand (HTD) curve [16] widely used as benchmark traffic assignment model [16,20,44,45]:…”

“…For modelling EV or ICV motion, a FSM with six states S= {S 0 , S 1 , S 2 , S 3 , S 4 , S 5 } (Fig. 3) is elaborated from the one presented in [20]. Besides the initial "start-up" state S 0 and "shut-down" state S 3 , we consider a "driving" state S 1 and a "queuing for traffic" state S 2 , a "charging" or "refilling" state S 4 and a "queuing for charging" or "queuing for refilling" state S 5 .…”

“…To do this, we propose a novel probabilistic scenario analysis framework that is based on an integrated model that accounts for the interconnections and interactions within the road-power SoS [17][18][19], and is used to quantify a set of performance metrics [20] under nominal condition and disruption scenarios [21,22]. The SoS model consists of a set of dynamically interacting models, namely, those of the road and electric power networks, and the vehicles fleet.…”

“…A realistic case study is considered, concerning the travel of both EVs and ICVs on a road network in New York State. Different levels of EVs penetration are considered and FSM is shown to provide a powerful modelling framework that can include various characteristics of the SoS dynamics, such as drivers attitudes, traffic congestion, and disruptive effects induced by traffic incidents on the EVs and ICVs motion [20]. For the performance of the integrated road-power infrastructure, with a hybrid fleet of EVs and ICVs under different disruption scenarios, in line with [38], we quantify the service losses related to delays that the vehicles may incur in the different accidental scenarios.…”