December 4, 2025SustainabilityOpen Access

A Multi-Objective Reinforcement Learning Framework for Energy-Efficient Electric Bus Operations

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HQHua QiuSouth China Agricultural University

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Implication

Simulation results reveal an 11.2% reduction in energy consumption for eco-driving strategies in electric buses, indicating improved operational sustainability.

Key Points

11.2% reduction in energy consumption demonstrated through simulation results, enhancing eco-driving strategies.
Analysis based on trajectory data collected from a 15.47 km route with 31 stops, highlighting energy consumption characteristics.
Multi-objective reinforcement learning framework utilized for optimizing energy efficiency and ride comfort in bus operations.
Findings support scalability in promoting energy saving autonomous driving technology for electric buses.

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Hua Qiu (2025) studied this question.

synapsesocial.com/papers/6930dc81ea1aef094cca258b https://doi.org/10.3390/su172310695

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Implication

Simulation results reveal an 11.2% reduction in energy consumption for eco-driving strategies in electric buses, indicating improved operational sustainability.

A Multi-Objective Reinforcement Learning Framework for Energy-Efficient Electric Bus Operations

Discussion

Member takes

Implication

Key Points

Cite This Study

A Multi-Objective Reinforcement Learning Framework for Energy-Efficient Electric Bus Operations

Discussion

Member takes

Implication

Key Points

Cite This Study

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