A Two-Layer Approach for Ecodriving under Traffic

Obereigner, Gunda; Polterauer, Philipp; Re, Luigi del

doi:10.23919/acc45564.2020.9147638

Cited by 9 publications

(1 citation statement)

References 8 publications

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“…The reference speed provided by offline planning without any disturbances is represented by Reference. The selected benchmarks include two eco-driving strategies similar to [47] and [34], which are incapable of lane changing and defined as:…”

Section: Simulations a Simulations Setupmentioning

confidence: 99%

A Less-Disturbed Ecological Driving Strategy for Connected and Automated Vehicles

Yang

Zhao

Jiang

et al. 2023

IEEE Trans. Intell. Veh.

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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%.Index Terms-Less-disturbed ecological driving, speed optimisation, dynamic programming, local adaptation NOMENCLATURE ∆sLongitudinal distance step size ∆v Difference in velocity between the controlled CAV and the objective vehicle on the target lane δ fFront wheel angle

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Section: Simulations a Simulations Setupmentioning

confidence: 99%

A Less-Disturbed Ecological Driving Strategy for Connected and Automated Vehicles

Yang

Zhao

Jiang

et al. 2023

IEEE Trans. Intell. Veh.

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A two‐layer control strategy for fuel‐efficient connected vehicles

Bentaleb,

Hajjaji,

Karama

et al. 2024

IET Intelligent Trans Sys

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This paper proposes a two‐layer control strategy to enhance connected vehicles fuel efficiency. Compared with previous works, this study proposes an approach to optimize both the vehicle speed and gearbox position to achieve better fuel efficiency. The control task is given in two stages: the upper layer and the lower layer. Before trip departure, the upper layer concurrently optimizes the vehicle speed and gearbox position based on road map information, and engine and vehicle parameters for an entire route. Then, while driving, the lower layer is used to follow the pre‐computed optimal profiles. Model predictive control follows the optimal speed while ensuring an adaptive safe distance constraint with a preceding vehicle. For gear shifting, an online shift control assuring the tracking of the optimal gear position is developed based on look‐ahead road data and vehicle actions. The effectiveness of the proposed control strategy was evaluated with comprehensive simulations and comparison tests using Matlab and CarSim software. The mean online optimization calculation time is 0.065 s, indicating its real‐time capability. The proposed method can be used as a driving assist system or implemented as a speed and gear controller for self‐driving vehicles.

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