Dynamic Pro-Active Eco-Driving Control Framework for Energy-Efficient Autonomous Electric Mobility

As autonomous vehicle technology advances, the development of energy-efficient control
methodologies emerges as a critical area in the literature. This includes the behavior control of vehicles
near signalized intersections, which still needs comprehensive exploration. Through connectivity, the
adoption of promising eco-driving approaches can manage a vehicle’s speed profile to improve energy
consumption. This study focuses on controlling the speed of an autonomous electric vehicle (AEV)
both up and downstream of a signalized intersection in the presence of preceding vehicles. In order
to achieve this, a dynamic pro-active predictive cruise control eco-driving (eco-PPCC) framework
is developed that, instead of merely reacting to the preceding vehicle’s speed changes, uses the
preceding vehicle’s upcoming data to actively adjust and optimize the speed profile of the AEV. The
proposed algorithm is compared to the conventional Gipps and eco-PCC models for benchmarking
and performance analysis through numerous scenarios. Additionally, real-world measurements are
performed and taken to consider practical use cases. The results demonstrate that when compared
to the two baseline methods, the proposed framework can add significant value to reducing energy
consumption, preventing unnecessary stops at intersections, and improving travel time.