Comparison of Electric Vehicle’s Energy Consumption Factors for Different Road Types

Abstract

Energy-optimal route planning for electric vehicle (EV) is highly required for the wide-spread use of EV, which is hindered by limited battery capacity and relative short cruising range. Obtaining the cost for each link (i.e., link energy consumption) in road networks plays a key role in energy-optimal route planning process. The link energy consumption depends mainly on energy consumption factor, which is related to not only vehicle speed but also road type. This study aims to analyze the difference of EV’s energy consumption factors for different road types. According to the floating car data (FCD) collected from the road network in Beijing, the vehicle specific power (VSP) distributions under different average travel speeds for different road types are analyzed firstly, and then the EV’s energy consumption rates under different VSP-Bins are calculated. By using VSP as an intermediate variable, EV’s energy consumption factor models for different road types are established and the difference of EV’s energy consumption factors is analyzed. The results show that road type-based energy consumption factor should be used in EV’s energy-optimal route planning process.

A Data-Driven Method for Energy Consumption Prediction and Energy-Efficient Routing of Electric Vehicles in Real-World Conditions

“…Energy consumption of an EV depends on the characteristics of the vehicle and its drivetrain, the drive cycle (the speed profile driven) and auxiliary consumption. In real-world driving, this speed profile-and therefore energy consumption-is extremely variable and dependent on both road characteristics [9,10], such as road type and altitude profile, and driving style [11,12]. Additionally, the speed profile is affected by a number of external influences, such as traffic [13], weather [14] and driver mood, which either influence the behavior of or impose a behavior on the driver and trigger the use of auxiliaries.…”

“…As reported in [16], energy estimation models are generally created for the purpose of EV drivetrain design and optimization [17,18], assessment of the influences on the energy consumption [10,19,20], global energy consumption or grid impact due to the introduction of EVs or hybrid vehicles [14,15], or (all-electric) range prediction [21]. Energy estimation for the purpose of range prediction either relies on vehicle simulations where drivetrains and vehicle behavior are being simulated [13,22], sometimes down to the component level, or statistical models.…”

Energy Consumption Prediction for Electric Vehicles Based on Real-World Data

“…EV energy consumption studies can generally be divided according to their purpose and model calculation methodology. Studies report the development of energy models for the purpose of EV drivetrain design and optimization [2,3], assessment of the influences on the energy consumption [4][5][6], and global energy consumption or grid impact due to the introduction of EV or hybrid vehicles [7,8]. In some cases the energy model is used for an (all-electric) range prediction [9].…”

“…In some cases the energy model is used for an (all-electric) range prediction [9]. The methodology for the calculation of energy consumption either consists of creating a vehicle model that simulates electrical parameters based on kinematic and dynamic requirements (backwards simulation) [3,[5][6][7] or by means of statistical models based on measurements of the EV consumption, either from real-world data [4,9] or test cycles [2]. Using real-world measurements has the advantage of predicting more realistic values for energy consumption, but relies on available data and statistical modeling and is often uncoupled from the vehicle dynamics and drivetrain behavior.…”

Stochastic frontier analysis of excess access to mid-trip battery electric vehicle fast charging

“…Lastly, travel patterns have been shown to be related to refueling behavior (Kitamura and Sperling, 1987), and so does the speed because it affects the electricity consumption of EVs (Yao et al, 2013). So this study includes explanatory variables of number of trips, Vehicle Miles of Travel (VMT), and speed to analyze fast-charging behavior.…”