Driving strategies in terms of acceleration or deceleration determine fuel consumption and energy recuperation (regeneration), especially for hybrid vehicles. Drivers, who do not always adapt optimal driving strategies, can be supported by technical systems that either recommend optimal strategies or provide an optimised longitudinal vehicle control, which is the focus of adaptive cruise control (ACC) systems. Within this paper, a methodology on how to quantify the fuel reduction potential for different driving strategies in ACC relevant driving scenarios is described. This methodology can be applied for different drive train concepts – hybrid vehicles as well as conventional drive train vehicles. Reference measurements of different drivers are recorded in order to determine the baseline for the quantification of probable fuel reduction potential against a realistic reference. The representative average driver for the relevant scenario is derived from these measurements. The reference profiles are examined with respect to different driver types and the velocity profiles are used as an input for the simulation of different driving strategies. A vehicle simulation model allows the calculation of fuel consumption as well as the determination of the state of charge of the hybrid battery, if applicable. The methodology is verified by means of driving tests with a test vehicle.
Methodology for quantification of fuel reduction potential for adaptive cruise control relevant driving strategies
IET Intelligent Transport Systems ; 8 , 1 ; 68-75
2014-02-01
8 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
energy recuperation , driver type , drivetrain concept , position control , energy regeneration , primary cells , adaptive cruise control , vehicle deceleration , longitudinal vehicle control , velocity profile , fuel reduction , first step reference measurement , ACC systems , fuel economy , ACC relevant driving scenario , driving test , hybrid vehicle , driving strategy , acceleration control , fuel consumption , hybrid electric vehicles , adaptive control , vehicle acceleration , hybrid battery
Metadata by IET is licensed under CC BY 3.0
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