Optimal Driving Range for Battery Electric Vehicles Based on Modeling Users’ Driving and Charging Behavior

Abstract

This paper proposes a simulation approach for the optimal driving range of battery electric vehicles (BEVs) by modeling the driving and charging behavior. The driving and charging patterns of BEV users are characterized by reconstructing the daily travel chain based on the practical data collected from Shanghai, China. Meanwhile, interdependent behavioral variables for daily trips and each trip are defined in the daily trip chain. To meet the goal of the fitness of driving range, a stochastic simulation framework is established by the Monte Carlo method. Finally, with consideration of user heterogeneity, the optimal driving range under different charging scenarios is analyzed. The findings include the following. (1) The daily trip chain can be reconstructed through the behavioral variables for daily trips and each trip, and there is a correlation between the variables examined by the copula function. (2) Users with different daily travel demand have a different optimal driving range. When choosing a BEV, users are recommended to consider that the daily vehicle kilometers traveled are less than 34% of the battery driving range. (3) Increasing the charging opportunity and charging power is more beneficial to drivers who are characterized by high daily travel demand. (4) On the premise of meeting travel demand, the beneficial effects of increased fast-charging power will gradually decline.

V2G Multi-Objective Dispatching Optimization Strategy Based on User Behavior Model

“…Pareto distribution represents the coordination of the two objective functions of grid load variance and electric vehicle user income. It can be seen that the distribution is uniform and stable, which verifies the feasibility of Pareto optimization to solve the multi-objective problem (Lu et al, 2020).…”

“…After obtaining the probability distribution of the daily mileage, according to the assumption of power consumption and the assumption that the battery is full at the departure time, the probability distribution of the initial battery SOC of the EVs at the end of the trip can be further obtained (Lu et al, 2020).…”

“…Assuming that the charging and discharging time and power of each EV are not affected by other EVs. Based on the Monte Carlo sampling method, according to the random probability distribution of each electric vehicle's driving conditions, battery characteristics, and expected SOC preferences (González-Garrido et al, 2019;Lu et al, 2020).…”

Accounting for BEV Users’ Risk Attitudes and Charging Inertia in En Route Charging Choice Behavior

“…Many studies have investigated BEV users' charging choice behavior, most of which can be divided into after-trip and en route charging choice behavior [7][8][9][10]. After-trip charging behavior investigations assume that BEV users have access to chargers at the destination, such as home, workplaces, entertainment areas, and shopping areas.…”

A Hybrid Method to Calculate the Real Driving Range of Electric Vehicles on Intercity Routes

“…Electric vehicle driving range can be obtained either by applying a simulation process based on the use of the associated algorithms [18][19][20][21][22], or from a self-learning process applying real data from previous experiences [23][24][25][26][27][28][29]. This second method, although more accurate, requires a precise knowledge of the driving conditions such as the route characteristics and style of driving, which are not always are available.…”