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1. A Vehicle Coordination and Charge Scheduling Algorithm for Electric Autonomous Mobility-on-Demand Systems

   Boewing, Felix; Schiffer, Maximilian; Salazar, Mauro; Pavone, Marco (March 2020, Proceedings of the American Control Conference)

   null (Ed.)

   This paper presents an algorithmic framework to optimize the operation of an Autonomous Mobility-on-Demand system whereby a centrally controlled fleet of electric self-driving vehicles provides on-demand mobility. In particular, we first present a mixed-integer linear program that captures the joint vehicle coordination and charge scheduling problem, accounting for the battery level of the single vehicles and the energy availability in the power grid. Second, we devise a heuristic algorithm to compute near-optimal solutions in polynomial time. Finally, we apply our algorithm to realistic case studies for Newport Beach, CA. Our results validate the near optimality of our method with respect to the global optimum, whilst suggesting that through vehicle-to-grid operation we can enable a 100% penetration of renewable energy sources and still provide a high-quality mobility service. 

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2. Urban scale vehicle-to-building-to-grid integration leveraging human mobility modeling for enhanced grid flexibility

   https://doi.org/10.1007/s12273-025-1346-3  

   Liu, Yapan; Dong, Bing (November 2025, Building Simulation)

   Abstract Across the U.S., the increasing demand for electric vehicle (EV) charging infrastructure is placing new demands on the power grid, challenging its stability and efficiency. To address these challenges, this study proposes a Vehicle-to-Building-to-Grid (V2B2G) framework that incorporates urban-scale human mobility modeling to optimize EV charging and discharging. Using anonymized GPS traces from the study community, we extracted individual mobility patterns and applied kernel-density estimation to predict departure and arrival times for each user. The framework was tested in a mixed-use community in Phoenix, Arizona that includes both residential and commercial buildings. A comprehensive decentralized model predictive control (MPC) framework is implemented to minimize energy costs and enhance grid flexibility through demand-side management while maintaining occupant comfort. Four different control strategies were designed and evaluated, the strategy which balances both user and grid benefits demonstrated the best performance, achieving: (1) a flattened grid net load curve, with a 56% reduction in on-peak demand and a 56% decrease in peak load rebound; (2) a 37.96% reduction in grid net load compared to the baseline control; and (3) a 68.05% performance improvement when considering six flexibility factors: cost savings, total-energy reduction, on-peak demand reduction, off-peak demand reduction, load shifting from peak to non-peak hours, and peak-load-rebound reduction. These findings enhance our understanding of the impacts of urban mobility and EV charging optimization on grid management. 

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3. Effect of Routing Constraints\! on Learning Efficiency of Destination Recommender Systems in Mobility-on-Demand Services

   https://doi.org/10.1109/TITS.2020.3038675  

   Yoon, Gyugeun; Chow, Joseph Y.; Dmitriyeva, Assel; Fay, Daniel (December 2020, IEEE Transactions on Intelligent Transportation Systems)

   null (Ed.)

   With Mobility-as-a-Service platforms moving toward vertical service expansion, we propose a destination recommender system for Mobility-on-Demand (MOD) services that explicitly considers dynamic vehicle routing constraints as a form of a ``physical internet search engine''. It incorporates a routing algorithm to build vehicle routes and an upper confidence bound based algorithm for a generalized linear contextual bandit algorithm to identify alternatives which are acceptable to passengers. As a contextual bandit algorithm, the added context from the routing subproblem makes it unclear how effective learning is under such circumstances. We propose a new simulation experimental framework to evaluate the impact of adding the routing constraints to the destination recommender algorithm. The proposed algorithm is first tested on a 7 by 7 grid network and performs better than benchmarks that include random alternatives, selecting the highest rating, or selecting the destination with the smallest vehicle routing cost increase. The RecoMOD algorithm also reduces average increases in vehicle travel costs compared to using random or highest rating recommendation. Its application to Manhattan dataset with ratings for 1,012 destinations reveals that a higher customer arrival rate and faster vehicle speeds lead to better acceptance rates. While these two results sound contradictory, they provide important managerial insights for MOD operators. 

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4. Privacy-Preserving Mechanisms for Coordinating Airspace Usage in Advanced Air Mobility

   https://doi.org/10.1145/3732290  

   Maheshwari, Chinmay; Mendoza, Maria G; Tuck, Victoria; Su, Pan-Yang; Qin, Victor L; Seshia, Sanjit; Balakrishnan, Hamsa; Sastry, Shankar (June 2025, ACM Journal on Autonomous Transportation Systems)

   Advanced Air Mobility (AAM) operations are expected to transform air transportation while challenging current air traffic management practices. By introducing a novel market-based mechanism, we address the problem of on-demand allocation of capacity-constrained airspace to AAM vehicles with heterogeneous and private valuations. We model airspace and air infrastructure as a collection of contiguous regions (or sectors) with constraints on the number of vehicles that simultaneously enter, stay, or exit each region. Vehicles request access to airspace with trajectories spanning multiple regions at different times. We use the graph structure of our airspace model to formulate the allocation problem as a path allocation problem on a time-extended graph. To ensure that the cost information of AAM vehicles remains private, we introduce a novel mechanism that allocates each vehicle a budget of “air-credits” (an artificial currency) and anonymously charges prices for traversing the edges of the time-extended graph. We seek to compute a competitive equilibrium that ensures that: (i) capacity constraints are satisfied, (ii) a strictly positive resource price implies that the sector capacity is fully utilized, and (iii) the allocation is integral and optimal for each AAM vehicle given current prices, without requiring access to individual vehicle utilities. However, a competitive equilibrium with integral allocations may not always exist. We provide sufficient conditions for the existence and computation of a fractional-competitive equilibrium, where allocations can be fractional. Building on these theoretical insights, we propose a distributed, iterative, two-step algorithm that: (1) computes a fractional competitive equilibrium, and (2) derives an integral allocation from this equilibrium. We validate the effectiveness of our approach in allocating trajectories for the emerging urban air mobility service of drone delivery. 

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5. PV to Vehicle, PV to Grid, Vehicle to Grid, and Grid to Vehicle Micro Grid System Using Level Three Charging Station

   https://doi.org/10.1109/GreenTech52845.2022.9772041  

   Balal, Afshin; Giesselmann, Michael (March 2022, 2022 IEEE Green Technologies Conference (GreenTech))

   This paper makes use of electric vehicles (EVs) that are simultaneously connected to the Photovoltaic Cells (PV) and the power grid. In micro-grids, batteries of the electric vehicles (EVs) used as a source of power to feed the power grid in the peak demands of electricity. EVs can help regulation of the power grid by storing excess solar energy and returning it to the grid during high demand hours. This paper proposes a new architecture of micro-grids by using a rooftop solar system, Battery Electric Vehicles (BEVs), grid connected inverters, a boost converter, a bidirectional half-bridge converter, output filter, including L, LC, or LCL, and transformers. The main parts of this micro-grid are illustrated and modeled, as well as a simulation of their operation. In addition, simulation results explore the charging and discharging scenarios of the BEVs. 

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