Impact of different spacing policies for adaptive cruise control on traffic and energy consumption of electric vehicles

Abstract

Abstract-This paper assesses the impact of different spacing policies for Adaptive Cruise Control (ACC) systems on traffic and environment. The largest deal of existing studies focus on assessing the performance in terms of safety, while only few deal with the effect of ACC on the traffic flow and the environment. In particular, very little is know on traffic stability and energy consumption. In this study, the vehicles equipped with ACC are modelled and controlled by two different spacing policies. Besides, Human Driving Behavior (HDB) is modelled by using Gipps model for comparison and for simulating different penetration rates. As distinguished from other studies, vehicle dynamics and energy consumption of an electric car is formulated, which has completely different characteristics and limitations than combustion engine cars. Hence the study aims at providing additional understanding of how ACC-equipped electric vehicles will behave in dense traffic conditions. HDB and ACC vehicles are placed in a roundabout at different penetration rates. String stability and energy consumption are investigated by giving a shock wave to a stable traffic condition. It is found that ACC with quadratic spacing policy has significantly positive effects on string stability and energy consumption.

Effects of ACC and CACC vehicles on traffic flow based on an improved variable time headway spacing strategy

“…In aspect of spacing strategy and control model: Bayar et al [13] study the linear spacing strategy and the quadratic spacing strategy under the mixed scenario of ACC and manual vehicles. The results show that the latter performs better in reducing energy consumption and travel time, and significantly improves the platoon stability.…”

Fast stochastic non-linear model predictive control for electric vehicle advanced driver assistance systems

“…[12], [15]). The statistics of the stochastic position of the preceding vehicle can be estimated by:…”

Risk-averse Stochastic Nonlinear Model Predictive Control for Real-time Safety-critical Systems

“…where d 0 is a constant minimum safe distance, and t hw is the desired time headway (Bayar et al, 2016). …”