Enhancing energy-environmental efficiency of autonomous bus: an NMPC-based trajectory optimization model

Document Type

Journal Article

Publication Date

2026

Subject Area

place - asia, place - urban, mode - bus, mode - bus rapid transit, technology - alternative fuels, technology - automatic vehicle monitoring, technology - emissions, policy - sustainable, policy - environment, planning - methods, planning - environmental impact

Keywords

Urban public transit, autonomous bus, model Predictive Control, trajectory optimization, energy-environmental efficiency, multi-objective optimization

Abstract

Public transportation increasingly prioritizes cost reduction and energy-environmental efficiency (EEE). However, limited researches address entire-route trajectory optimization for autonomous buses (ABs), particularly hybrid electric buses (HEBs) with unique operating states. In this study, an improved Newell car-following model is proposed to generate initial bus trajectories, and a Vehicle Specific Power (VSP)-based EEE quantification model is developed to compute HEB energy consumption and emissions. A rolling-optimization model based on Nonlinear Model Predictive Control (NMPC) is then established, integrating dynamic and kinematic principles to enhance EEE and reduce operating time. Numerical simulations utilize real-world Bus Rapid Transit data from Chengdu, China. Results demonstrate that the optimized AB trajectories reduce CO₂ emissions and energy consumption by 8.7% and 12.3%, respectively, compared with initial AB trajectories, and achieve 13.2% and 17.6% reductions compared with conventional buses. The proposed method also improves operating stability, underscoring the potential for sustainable and efficient urban transit.

Rights

Permission to publish the abstract has been given by Taylor&Francis, copyright remains with them.

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