This paper discusses the optimization of routing and charging infrastructure for Autonomous Mobility-on-Demand (AMoD) systems. With the advent of vehicle autonomy, connectivity, and electric powertrains, AMoD systems are becoming more feasible. To address this issue, the authors propose a modeling and optimization framework that considers both fleet activities and charging infrastructure placement. They adopt a mesoscopic planning perspective and develop a time-invariant model that captures routes and charging patterns while explicitly considering the state of charge of vehicles. The road network is represented as a digraph with iso-energy arcs to accurately represent energy consumption. The problem is formulated as a mixed-integer linear program, ensuring global optimality. The proposed framework can be solved in less than 10 minutes, making it practical for real-world applications. To validate their approach, two case studies using real-world taxi data from Manhattan, NYC are presented. In the first case study, the authors analyze the trade-off between charging infrastructure prevalence and empty-mileage driven by the fleet. They demonstrate that jointly optimizing infrastructure siting outperforms heuristic placement policies. Additionally, they find that increasing the number of stations is beneficial only up to a certain point. The second case study focuses on vehicle design and explores the impact of battery capacity on energy consumption. Surprisingly, deploying vehicles with smaller batteries results in lower energy consumption despite requiring more trips to charging stations. Fleets equipped with smaller batteries consume approximately 12% less energy compared to those with larger battery capacities. Overall, this paper provides valuable insights into optimizing routing and charging infrastructure for AMoD systems. The proposed modeling and optimization framework offers an efficient solution that can be applied to real-world scenarios. By considering both fleet activities and infrastructure placement, decision-makers can make informed choices to enhance efficiency in future transportation systems.
This is about making self-driving cars and taxis more efficient. They use a special program to figure out the best routes and places to charge the cars. They also study how much energy the cars use and how often they need to charge. They tested their program using real taxi data from New York City. They found that putting charging stations in certain places can make the cars use less energy.
Definitions
- Optimization: Finding the best way to do something.
- Routing: Figuring out the best path or route to take.
- Charging infrastructure: Places where electric vehicles can be charged.
- Autonomous Mobility-on-Demand (AMoD) systems: Self-driving cars or taxis that you can call for a ride.
- Modeling: Creating a computer program that represents real-life situations.
- Fleet activities: The actions and movements of a group of vehicles.
- Placement: Deciding where something should be located.
- Time-invariant model: A computer program that doesn't change over time.
- State of charge: How much battery power is left in a vehicle.
- Digraph: A type of graph used to represent connections between different points or locations.
- Iso-energy arcs: Lines on a graph that show how much energy is used when traveling between different points.
- Mixed-integer linear program: A type of math problem with both whole numbers and fractions, where the goal is to find the best solution.
- Global optimality: Finding the absolute best solution possible for a problem.
- Heuristic
Optimizing Routing and Charging Infrastructure for Autonomous Mobility-on-Demand Systems
Autonomous Mobility-on-Demand (AMoD) systems are becoming increasingly feasible with the advent of vehicle autonomy, connectivity, and electric powertrains. To address this issue, researchers have proposed a modeling and optimization framework that considers both fleet activities and charging infrastructure placement.
Background
The development of AMoD systems has been hindered by two main challenges: 1) how to efficiently route vehicles while considering energy consumption; 2) how to optimally place charging stations so as to minimize empty miles driven by the fleet. To address these issues, the authors propose a mesoscopic planning perspective that captures routes and charging patterns while explicitly considering the state of charge (SOC) of vehicles. The road network is represented as a digraph with iso-energy arcs to accurately represent energy consumption. The problem is formulated as a mixed integer linear program (MILP), ensuring global optimality.
Model Formulation
The model consists of three components: 1) Fleet Activity Model; 2) Charging Station Placement Model; 3) Energy Consumption Model. The Fleet Activity Model captures customer requests over time using discrete variables representing service start times for each request. The Charging Station Placement Model uses binary variables indicating whether or not each station should be placed at its corresponding location on the map. Finally, the Energy Consumption Model uses continuous variables representing SOC levels at various points along routes taken by vehicles in order to accurately capture energy consumption over time due to varying terrain conditions such as hills or curves in roads that require more energy than flat surfaces do when driving on them.
Case Studies
To validate their approach, two case studies using real world taxi data from Manhattan NYC were presented in the paper:
1.) In the first case study, they analyze tradeoffs between charging infrastructure prevalence and empty mileage driven by fleets equipped with different battery capacities ranging from 25 kWh – 75 kWh batteries respectively . They demonstrate that jointly optimizing infrastructure siting outperforms heuristic placement policies such as placing all stations within one mile radius around origin/destination locations or uniformly distributing them across city blocks . Additionally , they find out that increasing number of stations is beneficial only up till certain point after which it becomes redundant .
2.) The second case study focuses on vehicle design where they explore impact of battery capacity on energy consumption . Surprisingly , deploying vehicles with smaller batteries results in lower energy consumption despite requiring more trips to charging stations . Fleets equipped with smaller batteries consume approximately 12% less energy compared to those with larger battery capacities .
Conclusion
Overall , this paper provides valuable insights into optimizing routing &charging infrastructures for AMOD systems through proposing an efficient solution which can be applied practically in real world scenarios . By considering both fleet activities &infrastructure placements , decision makers can make informed choices towards enhancing efficiency in future transportation system designs
The advent of vehicle autonomy, connectivity and electric powertrains is expected to enable the deployment of Autonomous Mobility-on-Demand systems. Crucially, the routing and charging activities of these fleets are impacted by the design of the individual vehicles and the surrounding charging infrastructure which, in turn, should be designed to account for the intended fleet operation. This paper presents a modeling and optimization framework where we optimize the activities of the fleet jointly with the placement of the charging infrastructure. We adopt a mesoscopic planning perspective and devise a time-invariant model of the fleet activities in terms of routes and charging patterns, explicitly capturing the state of charge of the vehicles by resampling the road network as a digraph with iso-energy arcs. Then, we cast the problem as a mixed-integer linear program that guarantees global optimality and can be solved in less than 10 min. Finally, we showcase two case studies with real-world taxi data in Manhattan, NYC: The first one captures the optimal trade-off between charging infrastructure prevalence and the empty-mileage driven by the fleet. We observe that jointly optimizing the infrastructure siting significantly outperforms heuristic placement policies, and that increasing the number of stations is beneficial only up to a certain point. The second case focuses on vehicle design and shows that deploying vehicles equipped with a smaller battery results in the lowest energy consumption: Although necessitating more trips to the charging stations, such fleets require about 12% less energy than the vehicles with a larger battery capacity.
