Real-Time Energy-Optimal Path Planning for Electric Vehicles

The rapid adoption of electric vehicles (EVs) in modern transport systems has made energy-aware routing a critical task in their successful integration, especially within large-scale networks. In cases where an EV's remaining energy is limited and charging locations are not easily accessible, some destinations may only be reachable through an energy-optimal path: a route that consumes less energy than all other alternatives. The feasibility of such energy-efficient paths depends heavily on the accuracy of the energy model used for planning, and thus failing to account for vehicle dynamics can lead to inaccurate energy estimates, rendering some planned routes infeasible in reality. This paper explores the impact of vehicle dynamics on energy-optimal path planning for EVs. We develop an accurate energy model that incorporates key vehicle dynamics parameters into energy calculations, thereby reducing the risk of planning infeasible paths under battery constraints. The paper also introduces two novel online reweighting functions that allow for a faster, pre-processing free, pathfinding in the presence of negative energy costs resulting from regenerative braking, making them ideal for real-time applications. Through extensive experimentation on real-world transport networks, we demonstrate that our approach considerably enhances energy-optimal pathfinding for EVs in both computational efficiency and energy estimation accuracy.

Enhanced Methods for the Weight Constrained Shortest Path Problem: Constrained Path Finding Meets Bi-objective Search

The classic problem of \textit{constrained path finding} is a well-studied but yet challenging topic in AI with a broad range of applications in various areas such communication and transportation. The Weight Constrained Shortest Path Problem (WCSPP), as the base form of constrained path finding with only one side constraint, aims to plan a cost optimum path whose weight/resource usage is limited. Given the bi-criteria nature of the problem (i.e., dealing with cost and weight of paths), methods addressing the WCSPP have some common properties with bi-objective search. This paper leverages the recent state-of-the-art A*-based techniques in both constrained path finding and bi-objective search and presents two exact solution approaches to the WCSPP, both capable of solving hard problem instances on very large graphs. We empirically evaluate the performance of our algorithms on a new set of large and realistic problem instances and show their advantages over the state-of-the-art algorithms in both time and space metrics. This paper also investigates the importance of priority queues in constrained search with A*. We show with extensive experiments on both realistic and randomised graphs how bucket-based queues without tie-breaking can effectively improve the algorithmic performance of exhaustive bi-criteria searches.

Bi-objective Search with Bi-directional A*

Bi-objective search is a well-known algorithmic problem, concerned with finding a set of optimal solutions in a two-dimensional domain. This problem has a wide variety of applications such as planning in transport systems or optimal control in energy systems. Recently, bi-objective A*-based search (BOA*) has shown state-of-the-art performance in large networks. This paper develops a bi-directional variant of BOA*, enriched with several speed-up heuristics. Our experimental results on 1,000 benchmark cases show that our bi-directional A* algorithm for bi-objective search (BOBA*) can optimally solve all of the benchmark cases within the time limit, outperforming the state of the art BOA*, bi-objective Dijkstra and bi-directional bi-objective Dijkstra by an average runtime improvement of a factor of five over all of the benchmark instances.