Design and Power Consumption Scalability of an Autonomous Electric Robo-Car Using Different Adaptive Cruise Control Models
Open AccessAmong the topics associated with intelligent transportation systems, autonomous electric vehicles (AEVs) have received significant attention in research and application in the past decade; however, development, testing and data collection associated with such vehicles have been a great challenge due to various technical, policy and cost constraints. This paper focuses on an approach to construct, test, and collect data related to AEVs at a laboratory scaled-down level. The objective is to investigate AEV technologies while overcoming the real-world full-scale deployment and construction complexities. Accordingly, this paper presents a newly built scaled down AEV (i.e., Robo-Car) and studies the associated scalability in measured power consumption if compared to the estimated power consumption of a real-world AEV. The Intelligent Driving Model (IDM), the Non-linear Model Predictive Control (MPC) model and the Ecological Smart Driver Model (Eco-SDM) are implemented in the Robo-Car system as Adaptive Cruise Control (ACC) logics while a Polynomial Power Demand Model is adopted to measure energy consumption. Results show that the developed robot model can produce power consumption patterns comparable to those estimated for a scaled-down robot and a full-size AEV. Future research should explore the incorporation of additional models during different traffic condition (i.e., lane changing and merging scenarios) while using updated technologies in autonomous electric vehicles and searching for an optimal system integration.
- All rights reserved
Notice to Authors
If you are the author of this work and you have any questions about the information on this page, please use the Contact form to get in touch with us.