Robustness Generalizations of the Shortest Feasible Path Problem for Electric Vehicles

Electric Vehicle routing is often modeled as a Shortest Feasible Path Problem (SFPP), which minimizes total travel time while maintaining a non-zero State of Charge (SoC) along the route. However, the problem assumes perfect information about energy consumption and charging stations, which are difficult to even estimate in practice. Further, drivers might have varying risk tolerances for different trips. To overcome these limitations, we propose two generalizations to the SFPP; they compute the shortest feasible path for any initial SoC and, respectively, for every possible minimum SoC threshold. We present algorithmic solutions for each problem, and provide two constructs: Starting Charge Maps and Buffer Maps, which represent the tradeoffs between robustness of feasible routes and their travel times. The two constructs are useful in many ways, including presenting alternate routes or providing charging prompts to users. We evaluate the performance of our algorithms on realistic input instances

Electric Vehicle Route Planning in the Presence of Stochasticity

Electric Vehicle (EV) route planning is an important but hard problem, since battery capacity is limited, charging times are long, and charging stations are sparsely distributed. It is nonetheless critical to improving the time and energy efficiency of future transportation systems, and to promoting EV adoption, by reducing driver range anxiety. EV routing is usually modeled as a Shortest Feasible Path (SFP) problem, which ensures a non-negative State of Charge along the route, taking both travel time and energy consumption to be deterministic and known in advance. In practice, however, travel time and energy consumption are both stochastic variables, which can be hard to estimate accurately.This dissertation presents a set of techniques to advance our abilities to address such stochasticity. First, we show how to accurately predict energy consumption and travel times along routes using phases, a new structuring abstraction for vehicle speed profiles. Contrary to conventional wisdom, using phases outperforms even microscopic energy estimation models. We show that using phases to generate synthetic trips preserves the real-world variance in travel times and energy consumptions of real-world trips.Next, we show how to efficiently encapsulate the tradeoffs between travel times and robustness of feasible routes against deviations in energy consumptions using the Starting Charge Map and Buffer Map constructs. Further, we generalize the SFP problem to permit stochastic travel times and energy consumptions using two different probabilistic definitions of route feasibility. These definitions allow drivers to maintain route feasibility either in expectation, or by setting explicit lower bounds on stranding probability.We also study how to effectively apply well-known speedup techniques, such as Contraction and Edge Hierarchies, for route planning with stochastic edge weights. We show that the choice of weight representations has a significant impact on the routing query runtimes, and introduce the tiering technique, which significantly improves query times for three different stochastic routing objectives. We evaluate all presented methods on realistic routing instances. Lastly, we generalize the problem of identifying dwell regions for trajectory sets to that of finding shared dwell regions, and present two novel approaches to the problem. We show that our solutions outperform the state-of-the-art by nearly a factor of three

Stochastic Route Planning for Electric Vehicles

Electric Vehicle routing is often modeled as a generalization of the energy-constrained shortest path problem, taking travel times and energy consumptions on road network edges to be deterministic. In practice, however, energy consumption and travel times are stochastic distributions, typically estimated from real-world data. Consequently, real-world routing algorithms can make only probabilistic feasibility guarantees. Current stochastic route planning methods either fail to ensure that routes are energy-feasible, or if they do, have not been shown to scale well to large graphs. Our work bridges this gap by finding routes to maximize on-time arrival probability and the set of non-dominated routes under two criteria for stochastic route feasibility: ?-feasibility and p-feasibility. Our ?-feasibility criterion ensures energy-feasibility in expectation, using expected energy values along network edges. Our p-feasibility criterion accounts for the actual distribution along edges, and keeps the stranding probability along the route below a user-specified threshold p. We generalize the charging function propagation algorithm to accept stochastic edge weights to find routes that maximize the probability of on-time arrival, while maintaining ?- or p-feasibility. We also extend multi-criteria Contraction Hierarchies to accept stochastic edge weights and offer heuristics to speed up queries. Our experiments on a real-world road network instance of the Los Angeles area show that our methods answer stochastic queries in reasonable time, that the two criteria produce similar routes for longer deadlines, but that ?-feasibility queries can be much faster than p-feasibility queries