Pseudospectral convex optimization for on-ramp merging control of connected vehicles

Shi, Yang; Wang, Zhenbo; Wang, Chieh; Shao, Yunli

doi:10.1016/j.jfranklin.2023.08.017

Title: Pseudospectral convex optimization for on-ramp merging control of connected vehicles

Journal Article · Tue Aug 22 00:00:00 EDT 2023 · Journal of the Franklin Institute

DOI:https://doi.org/10.1016/j.jfranklin.2023.08.017· OSTI ID:2205418

^[1];

^[2]; Shao, Yunli ^[2]

University of Tennessee, Knoxville, TN (United States)
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

It can be a daunting task for human drivers to merge into highways because of the intricate vehicle negotiations and potential risk within limited time and space. Connected vehicle (CV) technologies could be a solution to this problem and offer many benefits to the road safety, traffic mobility, and energy efficiency. However, real-time optimal control of CVs is still an open challenge, due to the nonlinear vehicle dynamics, non-convex fuel consumption model, and highly dynamic uncertain inter-vehicle interactions. To tackle these issues, a novel real-time optimal control approach that balances the computational efficiency and solution optimality is proposed for the purpose of onboard application. To this end, the pseudospectral collocation method is integrated with a sequential convex programming approach to develop two new optimization algorithms, which are implemented within a model predictive control (MPC) framework to allow for real-time generation of optimal merging speed profiles. One algorithm leverages the line search technique to improve convergence, and the other benefits from the trust region method for better computational efficiency. The optimality and convergence process of both proposed algorithms are investigated by comparing their solutions with a popular non-linear solver. Furthermore, simulation results show that the proposed methods outperform the benchmark in terms of computational cost, fuel consumption, and traffic efficiency. In particular, the proposed fuel-economy merging rule can save 57.1% fuel consumption on average on four different traffic volumes. Meanwhile, the proposed optimal control algorithms can reduce 2.2% travel time on average comparing to the “first-in-first-out” merging rule.

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Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: USDOE Office of Energy Efficiency and Renewable Energy (EERE)

Grant/Contract Number:: AC05-00OR22725

OSTI ID:: 2205418

Journal Information:: Journal of the Franklin Institute, Vol. 360, Issue 15; ISSN 0016-0032

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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