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Eco-Driving Control of Connected and Automated Hybrid Electric Vehicles on Multi-lane Roads Using Model Predictive Control

Journal Article
2021-01-0780
ISSN: 2641-9637, e-ISSN: 2641-9645
Published April 06, 2021 by SAE International in United States
Eco-Driving Control of Connected and Automated Hybrid Electric Vehicles on Multi-lane Roads Using Model Predictive Control
Sector:
Citation: Khosravinia, K., Wang, S., and Lin, X., "Eco-Driving Control of Connected and Automated Hybrid Electric Vehicles on Multi-lane Roads Using Model Predictive Control," SAE Int. J. Adv. & Curr. Prac. in Mobility 3(4):1748-1756, 2021, https://doi.org/10.4271/2021-01-0780.
Language: English

Abstract:

The core idea of advanced eco-driving is to optimize the vehicle’s speed and acceleration profile from the energy point of view using real-time data from the vehicle to vehicle (V2V) and vehicle to infrastructure (V2I). However, the main assumption of most of the existing advanced eco-driving approaches is that vehicles are maintained on a single-lane road that considers only the longitudinal motion of the vehicle. In multi-lane roads, controlling the lateral movement of the vehicle or the dynamic lane-changing along with the longitudinal movement can have a positive effect on traffic flow, travel time, fuel economy, and exhaust emission of the vehicle. This paper presents a bi-level model predictive control strategy for connected and automated hybrid electric vehicles (CAHEVs) to optimize inter-vehicle safety, energy-saving, and emission reduction while considering both the lateral and longitudinal motions of the vehicle. The proposed control strategy consists of two control levels: 1) the calculation and optimization of the power distribution between the internal combustion engine and an electric motor, which is referred to as the low-level control, and 2) the optimization of the vehicle speed profile, which is the high-level control. Both levels are used to control the longitudinal motion of the vehicle. Lateral motion or lane changing decision is controlled in another control layer based on the energy consumption prediction on each lane. The proposed control strategy is evaluated under different driving conditions in a realistic urban traffic simulation environment in SUMO. Simulation results show a 6.18 % reduction in the fuel economy while the state of charge of the battery maintained in the standard range and 5.94%, 5.01%, and 5.09% decrease in hydrocarbon (HC), carbon monoxide (CO), nitrogen oxides (NOx) respectively compared to the bi-level MPC-based controller without lane-changing approach.