Abstract
This paper proposes a less-disturbed ecological driving strategy for connected and automated vehicles (CAVs). The proposed strategy integrates the offline planning and the online tracking. In offline planning, an energy efficient reference speed is created based on traffic information (such as the average traffic speed) and characteristics of the vehicle (such as the engine efficiency map) via dynamic programming. The consideration of average traffic speed in speed planning avoids selfish optimisations. In online tracking, model predictive control is employed to update the vehicle speed in real-time to track the reference speed. A key challenge in applying ecological driving strategies in real driving is that the vehicle has to consider other traffic participants when tracking the reference speed. Therefore, this paper combines both longitudinal control and lateral control to achieve better speed tracking by overtaking the preceding vehicle when necessary. The proposed less-disturbed ecological driving strategy has been evaluated in simulations in both single road segment scenario and real traffic environment. Comparisons of the proposed method with benchmark strategies and human drivers are made. The results demonstrate that the proposed less-disturbed ecological driving strategy is more effective in energy saving. Compared to human drivers, the less-disturbed eco-driving strategy improves the fuel efficiency of CAVs by 4.53%.
Highlights
Due to the shortage of fossil fuels and rising globally environmental concerns, improving the energy efficiency of transportation systems has become essential over the years
The time of a Connected and automated vehicles (CAVs) passing the intersection can be written as cip = mod ciperiod where mod is the modulo operation; cip is the signal cycle time when the vehicle passes the ith intersection; cib is the signal cycle time when the vehicle begins on the route and ciperiod is the total length of the traffic signal cycle period at the ith intersection; and the vehicle passing the ith intersection in vehicle traveling time period is represented by Tpi
The fuel consumption of a vehicle depends on the characteristics of its powertrain, road conditions, and traffic conditions
Summary
Due to the shortage of fossil fuels and rising globally environmental concerns, improving the energy efficiency of transportation systems has become essential over the years. The global optimisation optimised vehicle speed for the entire route to achieve the optimal energy efficiency It is completed offline before vehicle’s departure. Real-time optimisation based eco-driving strategies are developed considering uncertainties from other vehicles. Ecological adaptive cruise control (eco-ACC) is a typical real-time optimisation based ecodriving strategy, in which model predictive control (MPC) has been widely used. The eco-ACC enables more flexible spacing than the conventional ACC, allowing CAVs running at an optimised speed This guarantees that a higher energy efficiency is achieved and a safe distance is maintained simultaneously. The design eliminates the conflict of optimisations in both offline planning and online tracking This guarantees that the proposed method always generates the solution to improve energy efficiency while maintaining the vehicle safety.
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