Dispatching methods and systems for pure electric buses with hybrid charging modes

By constructing a mixed-integer linear programming model and combining battery swapping stations and pantograph charging facilities, the optimal charging strategy is dynamically selected, which solves the bottleneck of charging mode for pure electric buses, realizes the complementary advantages of multiple modes, reduces operating costs, and improves operating efficiency and timetable reliability.

CN122088892APending Publication Date: 2026-05-26BEIJING JIAOTONG UNIV
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
BEIJING JIAOTONG UNIV
Filing Date
2025-12-29
Publication Date
2026-05-26

AI Technical Summary

Technical Problem

The existing charging modes for pure electric buses suffer from bottlenecks in battery technology, charging infrastructure configuration, and operation scheduling, resulting in high operating costs, unstable timetables, and difficulty in large-scale adoption.

Method used

The scheduling method using a hybrid charging mode constructs a hybrid integer linear programming model, combines battery swapping stations and pantograph charging facilities, dynamically selects the optimal charging strategy, optimizes vehicle purchase, charging and battery swapping costs, and ensures power balance and operational continuity.

Benefits of technology

It achieves deep integration of multiple charging modes, reduces total cost, improves operational efficiency and timetable reliability, solves the limitations of relying on a single mode, achieves full coverage and synergy in cost dimensions, and avoids charging delays or power waste.

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Abstract

This invention provides a method and system for scheduling pure electric buses with a hybrid charging mode. It transforms a complex operational scheduling problem into a mathematical optimization problem with the objective of minimizing total cost, constrained by multiple real-world conditions such as time, energy, and resources. The system first coordinates infrastructure deployment at the physical level: deploying battery swapping stations at route endpoints to utilize the natural intervals between vehicle scheduling for rapid energy reset; and deploying pantograph charging facilities at suitable intermediate stops to utilize passenger stop times for "opportunistic charging." Next, at the decision-making level, a mixed-integer linear programming model is constructed. This model includes considerations such as vehicle energy consumption patterns, dynamic changes in battery capacity, continuous connection of bus routes, and limitations on the number of charging facilities. Finally, a mathematical optimization solver solves the model, outputting the cost-optimal vehicle scheduling plan and hybrid charging strategy.
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