Method for estimating the energy consumption of electric vehicles and plug‐in hybrid electric vehicles under real‐world driving conditions

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.…”

“…In contrast, using a vehicle model gives you a direct link with the vehicle dynamics and drivetrain behavior, making the identification of influences of the drivetrain parameters on energy consumption more clear. Although Shankar et al [4] and Neaimeh et al [9] both use real-world measurements for the calculation of the energy consumption, only a limited number of external parameters are included in their models and the link with the vehicle dynamics is low. In Neaimeh et al [9], the prediction was based on a simplified vehicle dynamics model and only included road inclination as an external influencing parameter, whereas Shankar et al [4] use a purely statistical approach based on road type detection and its linked average consumption.…”

“…Although Shankar et al [4] and Neaimeh et al [9] both use real-world measurements for the calculation of the energy consumption, only a limited number of external parameters are included in their models and the link with the vehicle dynamics is low. In Neaimeh et al [9], the prediction was based on a simplified vehicle dynamics model and only included road inclination as an external influencing parameter, whereas Shankar et al [4] use a purely statistical approach based on road type detection and its linked average consumption. This paper proposes to extend those models and fill this knowledge gap by including more external parameters while increasing the link with the vehicle dynamics.…”

An Intelligent Driver Alerting System for Real-Time Range Indicator Embedded in Electric Vehicles

“…Even taking into consideration the new low-cost generation of electric vehicle batteries, the range anxiety can still limit the usage of electric vehicles. Recently, a great deal of effort has been devoted to the study of reducing range anxiety by improving the SoC/range estimation methods in the vehicle battery monitoring systems using low cost microcontrollers [2], taking environmental conditions into consideration [3] [4], driver behavior [3] [5] [6], traffic conditions [6]- [7] and, time-variant efficiency analysis of the drive system [8]- [9]. There is still a lack, however, of studies into the communication between the vehicle battery monitoring systems and the electric vehicle drivers.…”