Junyan Su (苏隽岩)

Selected publications

1. Minimizing Emission for Timely Heavy-Duty Truck Transportation

   Junyan Su, Runzhi Zhou, Qingyu Liu, Wenjie Xu, Minghua Chen, and Haibo Zeng

   IEEE Transactions on Intelligent Transportation Systems accepted for publication

   Abs

   We consider the problem of minimizing emission of a heavy-duty truck transporting freight between two locations subject to a hard deadline constraint. The truck is equipped with a multi-speed transmission and a modern combustion engine that intelligently switches among multiple fuel injection strategies at certain engine speeds (called switching speeds) to achieve lower emission profiles. Our objective is to minimize the emission by optimizing both path and speed planning for heavy-duty trucks with multi-speed transmission and multiple injection strategies in the engine. This emission minimization problem, while pervasive in practice, has two challenges: i) the emission rate function is discontinuous and non-convex due to switching of the fuel injections and gear ratios, which makes the common practice of driving at a constant speed on a road segment not eco-friendly; ii) the problem is NP-hard due to the combinatorial nature of the simultaneous path and speed planning. We tackle the first challenge by considering the case where the truck can travel at a heterogeneous speed profile over a road segment and then formulate the speed planning problem as a convex problem. We further identify special structures in this problem and provide an efficient method for computing the optimal speed profile. We then tackle the second challenge by developing an efficient heuristic for both path planning and speed planning to solve the emission minimization problem on the scale of national highway systems. Our extensive simulations on the US highway system show that our solution reduces up to 46% NOx emission as compared to the commonly-adopted fastest path approach. We also find that optimizing heterogeneous speed profiles reduce up to 32% emission as compared to their homogeneous counterpart, thus are necessary to be considered in eco-friendly truck operations.

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

   Qiulin Lin, Junyan Su, and Minhua Chen

   In Proceedings of IEEE INFOCOM 2024

   Abs PDF

   We consider the scenario where an energy harvesting source sends its updates to a receiver. The source optimizes its energy allocation over a decision period to maximize a sum of time-varying functions of the age of information (AoI), representing the value of providing timely information. In a practical online setting, we need to make irrevocable energy allocation decisions at each time while the time-varying functions and the energy arrivals are only revealed sequentially. The problem is then challenging as 1) we are facing uncertain energy harvesting arrivals and time-varying functions, and 2) the energy allocation decisions and the energy harvesting process are coupled due to the capacity-limited battery. In this paper, we develop an optimal online algorithm CR-Reserve and show it achieves (lnθ+1)-competitive, where θis a parameter representing the level of uncertainty of the time-varying functions. It is the optimal competitive ratio among all deterministic and randomized online algorithms. We conduct simulations based on real-world traces and compare our algorithms with conceivable alternatives. The results show that our algorithms achieve 12% performance improvement as compared to the state-of-the-art baseline.,

3. 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.

4. 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.