Figure 3: Nature Energy

Fig. 3: Validation of PEV mobility estimation and calibration of PEV charging behaviour.

From: Planning for electric vehicle needs by coupling charging profiles with urban mobility

Fig. 3

a, Number of PEVs in the residential ZIP codes of the Bay Area from the simulation and the CVRP datasets in the end of 2013. The total number of PEVs is 15,963 from our simulation, which is close to the actual number from CVRP datasets, 16,103. b, Correlation between the simulated PEV and the PEV from CVRP data. c,d, Probability distributions of commuting distances, D, and commuting travel times, T, of all vehicle trips and EV trips estimated through income information and trip distances. e, Fractions of four types of mobility motif of all commuters and PEV users. 58% of commuters only travel between home and work on weekdays, while the rest 42% have other activities before or after work. Similarly, 66% of commuters using PEVs only travel between home and work, and the rest 34% have other activities before or after work. f, Probability distributions of charging energy ES obtained from charging sessions compared to those of the energy demand estimated by energy consumption model on three charging behaviour scenarios, morning, daily and two-days. g, Probability distributions of charging energy ES and those of the daily energy demand of simulated PEVs for the ZIP codes that have the most charging session records.