Junyan Su (苏隽岩)

Selected publications

1. Competitive Online Age-of-Information Optimization for Energy Harvesting Systems

   Qiulin Lin, Junyan Su, and Minhua Chen

   In Proceedings of IEEE INFOCOM 2024
2. Minimizing Carbon Footprint for Timely E-Truck Transportation: Hardness and Approximation Algorithm

   Junyan Su, Qiulin Lin, Minghua Chen, and Haibo Zeng

   In Proceedings of IEEE Conference on Decision and Control (CDC) 2023

   Abs arXiv PDF Website

   Carbon footprint optimization (CFO) is important for sustainable heavy-duty e-truck transportation. We consider the CFO problem for timely transportation of e-trucks, where the truck travels from an origin to a destination across a national highway network subject to a deadline. The goal is to minimize the carbon footprint by orchestrating path planning, speed planning, and intermediary charging planning. We first show that it is NP-hard even just to find a feasible CFO solution. We then develop a (1+\epsilon_F, 1+\epsilon_β) bi-criteria approximation algorithm that achieves a carbon footprint within a ratio of (1+\epsilon_F) to the minimum with no deadline violation and at most a ratio of (1+\epsilon_β) battery capacity violation (for any positive \epsilon_F and \epsilon_β). Its time complexity is polynomial in the size of the highway network, 1/\epsilon_F, and 1/\epsilon_β. Such algorithmic results are among the best possible unless P=NP. Simulation results based on real-world traces show that our scheme reduces up to 11% carbon footprint as compared to baseline alternatives considering only energy consumption but not carbon footprint.

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

   Junyan Su, Qiulin Lin, and Minghua Chen

   In Proceedings of the ACM e-Energy 2023

   Abs arXiv PDF Website Best Paper Award

   We study the carbon footprint optimization (CFO) of a heavy-duty e-truck traveling from an origin to a destination across a national highway network subject to a hard deadline, by optimizing path planning, speed planning, and intermediary charging planning. Such a CFO problem is essential for carbon-friendly e-truck operations. However, it is notoriously challenging to solve due to (i) the hard deadline constraint, (ii) positive battery state-of-charge constraints, (iii) non-convex carbon footprint objective, and (iv) enormous geographical and temporal charging options with diverse carbon intensity. Indeed, we show that the CFO problem is NP-hard. As a key contribution, we show that under practical settings it is equivalent to finding a generalized restricted shortest path on a stage-expanded graph, which extends the original transportation graph to model charging options. Compared to alternative approaches, our formulation incurs low model complexity and reveals a problem structure useful for algorithm design. We exploit the insights to develop an efficient dual-subgradient algorithm that always converges. As another major contribution, we prove that (i) each iteration only incurs polynomial-time complexity, albeit it requires solving an integer charging planning problem optimally, and (ii) the algorithm generates optimal results if a condition is met and solutions with bounded optimality loss otherwise. Extensive simulations based on real-world traces show that our scheme reduces up to 28% carbon footprint compared to baseline alternatives. The results also demonstrate that e-truck reduces 56% carbon footprint than internal combustion engine trucks.