Energy and Operational Efficiency of Shared Autonomous Fleet Powered with Battery Electric and Internal Combustion Engine Technologies McMaster University

Format:

Book

Conference/Event

Author/Creator:

Tang, Kang, author.

Abdulsattar, Harith, author.

Yang, Hao, author.

Wang, Jinghui, author.

Conference Name:

WCX SAE World Congress Experience (2026-04-14 : Detroit, Michigan, United States)

Language:

English

Subjects (All):

Internal combustion engines.

Electric vehicles.

Life cycle analysis.

Autonomous vehicles.

Energy consumption.

Energy conservation.

Fleets.

Energy management.

Batteries.

Local Subjects:

Internal combustion engines.

Electric vehicles.

Life cycle analysis.

Autonomous vehicles.

Energy consumption.

Energy conservation.

Fleets.

Energy management.

Batteries.

Physical Description:

1 online resource

Place of Publication:

Warrendale, PA SAE International 2026

Summary:

Electrifying shared autonomous fleets (Robotaxis) presents challenges in balancing decarbonization, service quality, and operational costs, given the limited driving range, long charging times, and suboptimal planning of charging infrastructure. This study develops an integrated energy management and fleet dispatching simulation framework to support cost-effective, low-carbon Robotaxi deployment. The proposed system models both battery electric vehicles (BEV) and internal combustion engine vehicles (ICEV) technologies, and is extensible to other powertrain types. The study also integrates a life cycle assessment module to evaluate well-to-wheel carbon emissions. A total of 1,440 scenarios are designed to test the performance of two service modes (ride-hailing vs. ride-pooling) in terms of energy consumption, emissions, service quality, and operational costs, across varying levels of trip demand and market penetration of different powertrain technologies. The testing aims to verify the system's effectiveness in improving energy efficiency, clarify the cost of autonomous vehicles electrification, and identify the most cost-effective low-carbon fleet composition under different scenarios. The results demonstrate that ride-pooling system outperforms both ride-hailing and private vehicles. Ride-pooling achieves 1525% lower carbon intensity and 1825% energy savings compared to private vehicles. It is also found that EVs present, on average, an 812% higher trip rejection rate than ICE fleets, demonstrating that electrifying Robotaxis comes at the cost of reduced service levels or increased costs. The study ultimately finds that electrifying Robotaxis at a moderate level (4060%) can achieve a good trade-off between environmental benefits, service quality, and cost

Notes:

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