Follow the Sun and Go with the Wind: Carbon Footprint Optimized Timely E-Truck Transportation

Joint work with Qiulin Lin, Minghua Chen, and Haibo Zeng

This project is a follow-up project of Energy-Efficient Timely Transportation of Long-Haul Heavy-Duty Truck.

Highlights

  • Accepted for publication in Nature Communications.
  • Best paper award at ACM e-Energy 2023.
  • We study an important yet challenging problem , namely the carbon footprint optimization problem for e-trucks.
  • We provide a novel problem formulation, which incurs low model complexity and reveals a useful problem structure. We develop an efficient algorithm with performance guarantee.
  • Key technical idea: The problem is easy either when there is no charging or when there is only charging. Our idea is to separate the combined challenging problem into those two easy subproblems and combine their solutions.
  • Key messages from evaluation: Our method complements the 36% carbon reduction from electrification with an additional 25% decrease, totaling a 61% reduction. With our method, we can achieve comparable carbon reductions 9 years sooner than zero-emission truck adoption alone.

Description

Electrifying heavy-duty trucks is crucial for decarbonizing transportation, but maximizing their potential requires minimizing the carbon footprint of timely deliveries. This complex optimization task involves strategic path, speed, and charging planning, which traditional methods struggle to optimize at scale. We present a novel stage-expanded graph formulation that reduces complexity and reveals a useful problem structure. Our formulation naturally decomposes the problem into more tractable subproblems, allowing efficient coordination between routing and charging decisions, and maintains a manageable graph size. We exploit these structural insights to design an efficient algorithm with strong performance guarantees. Simulations using real-world data over the U.S. highway system demonstrate that our method complements the 36% carbon reduction from electrification with an additional 25% decrease, totaling a 61% reduction. Moreover, our carbon-optimized strategy, applicable to various truck types, can achieve comparable carbon reductions 9 years sooner than zero-emission truck adoption alone. This approach significantly accelerates transportation decarbonization, offering a powerful tool in the fight against climate change.

Publications

Notes on Difference

E-Energy’23 [1] is one of the first work that gives the problem formulation for the CFO problem and gives an efficient algorithm for solving CFO. CDC’23 [2] supplements e-Energy’23 [1] by studying the theoretic hardness of CFO and provide a bi-criteria approximation algorithm with stronger theoretic performance guarantee (but much higher run-time complexity). In our journal paper NC’25 [3], we provide an improved algorithm and more comprehensive evaluations and discussions. The journal version NC’25 [3] also focuses more on the practical significance of CFO and its connection to the broader climate change framework. The poster and demo papers [4,5] give some preliminary results in the early stage of this project.

Conference Papers

[1] J. Su , Q. Lin, and M. Chen, “Follow the Sun and Go with the Wind: Carbon Footprint Optimized Timely E-Truck Transportation”, in Proceedings of 14th International Conference on Future Energy Systems (ACM e-Energy 2023), Orlando, Florida, June 20 - 23, 2023. (Best Paper Award). [pdf] [arxiv]

[2] J. Su, Q. Lin, M. Chen, and H. Zeng, “Minimizing Carbon Footprint for Timely E-Truck Transportation: Hardness and Approximation Algorithm”, (invited), in Proceedings of the 62th IEEE Conference on Decision and Control (CDC), Singapore, December 13-15, 2023. [arxiv]

Journal Articles

[3] J. Su, Q. Lin, and M. Chen, “Optimizing Carbon Footprint in Long-Haul Heavy-Duty E-Truck Transportation”, Nature Communications, accepted for publication, 2025.

Poster and Demo Papers

[4] J. Su, M. Chen, and H. Zeng, “Energy Efficient Timely Transportation : A Comparative Study of Internal Combustion Trucks and Electric Trucks”, in Proceedings of the 8th ACM International Conference on Systems for Energy-Efficient Built Environments, Cities, and Transportation (ACM BuildSys 2021), Coimbra, Portugal, November 17-18, 2021. (poster paper) [pdf]

[5] J. Su, M. Chen, and H. Zeng, “E2Pilot: An Energy-Efficient Navigation System for Long-haul Timely Truck Transportation”, in Proceedings of 13th International Conference on Future Energy Systems (ACM e-Energy 2022), virtual conference, June 28 - July 1, 2022. (demo paper) [pdf]

Patent

[6] M. Chen, J. Su, and Q. Lin, “Carbon Footprint Optimized Timely E-Truck Transportation”, 14 Aug 2025, U.S. Patent No. US2025/0258006.

Thesis

[7] “Minimizing Emission and Carbon Footprint for Timely Heavy-Duty Truck Transportation”, Ph.D. Dissertation, City University of Hong Kong, 2025. [link] [pdf]