Predictive energy-efficient driving strategy design of connected electric vehicle among multiple signalized intersections

Abstract

Signalized intersections dominate traffic flow in urban areas, resulting in increased energy consumption and travel delay for the vehicles involved. To mitigate the negative effect of traffic lights on eco-driving control of electric vehicles, a multi-intersections-based eco-approach and departure strategy (M-EAD) is proposed to improve vehicle energy efficiency, traffic throughput, and battery life, while maintaining acceptable driving comfort. M-EAD is a two-stage control scheme that includes efficient green signal window planning and speed trajectory optimization. In the upper stage, the traffic light green signal window planning is formulated as a shortest path problem, which is solved using an A* algorithm for travel delay reduction. In the lower stage, the speed optimization problem is solved by resorting to a receding horizon framework, in which the energy consumption and battery life losses are minimized using an iterative dynamic programming algorithm. Finally, Monte Carlo simulation with randomized traffic signal parameters is conducted to evaluate the performance of the proposed M-EAD strategy. The results show the various advancements of the proposed M-EAD strategy over two benchmark methods, constant speed and isolated-intersection-based eco-approach and departure strategies in terms of energy efficiency, travel time, and battery life in stochastic traffic scenarios. In addition, the performance of M-EAD on actual road conditions is validated by on-road vehicle test.