What question did this study set out to answer?

This research aims to develop an efficient charging strategy and accurately size the electric bus fleet considering traffic conditions.

January 14, 2026InventionsOpen Access

Optimization of Electric Bus Charging and Fleet Sizing Incorporating Traffic Congestion Based on Deep Reinforcement Learning

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Modeling optimizes charging and fleet size for electric buses, indicating effective logistics for public transport.

This research aims to develop an efficient charging strategy and accurately size the electric bus fleet considering traffic conditions.
Proposed an optimized charging strategy using deep reinforcement learning.
Integrated real-time traffic congestion and route characteristics into the model.
Modeled scenarios based on Beijing Bus Route 400 for practical applicability.
The DRL method minimizes charging time while ensuring operational completion.
In multi-route scenarios, a 40% increase in electric buses is needed without fast-charging technology.
This requirement decreases to 24% when fast-charging technology is included.

Yan et al. (2026) studied this question.

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