Mauro Salazar Villalon


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Salazar Villalon

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Mauro

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Publications 1 - 7 of 7
  • Intermodal Autonomous Mobility-on-Demand
    Item type: Journal Article
    Salazar Villalon, Mauro; Lanzetti, Nicolas; Rossi, Federico; et al. (2020)
    IEEE Transactions on Intelligent Transportation Systems
    In this paper we study models and coordination policies for intermodal Autonomous Mobility-on-Demand (AMoD), wherein a fleet of self-driving vehicles provides on-demand mobility jointly with public transit. Specifically, we first present a network flow model for intermodal AMoD, where we capture the coupling between AMoD and public transit and the goal is to maximize social welfare. Second, leveraging such a model, we design a pricing and tolling scheme that allows the system to recover a social optimum under the assumption of a perfect market with selfish agents. Third, we present real-world case studies for the transportation networks of New York City and Berlin, which allow us to quantify the general benefits of intermodal AMoD, as well as the societal impact of different vehicles. In particular, we show that vehicle size and powertrain type heavily affect intermodal routing decisions and, thus, system efficiency. Our studies reveal that the cooperation between AMoD fleets and public transit can yield significant benefits compared to an AMoD system operating in isolation, whilst our proposed tolling policies appear to be in line with recent discussions for the case of New York City. © 2000-2011 IEEE.
  • Tsao, Matthew; Milojevic, Dejan; Ruch, Claudio; et al. (2019)
    2019 International Conference on Robotics and Automation (ICRA)
  • Robuschi, Nicolo; Salazar Villalon, Mauro; Viscera, Nicola; et al. (2020)
    IEEE Transactions on Vehicular Technology
    This paper presents models and optimization algorithms to compute the fuel-optimal energy management strategies for a parallel hybrid electric powertrain on a given driving cycle. Specifically, we first identify a mixed-integer model of the system, including the engine on/off signal and the gear-shift commands. Thereafter, by carefully relaxing the fuel-optimal control problem to a linear program, we devise an iterative algorithm to rapidly compute the minimum-fuel energy management strategies including the optimal gear-shift trajectory. We validate our approach by comparing its solution with the globally optimal one obtained solving the mixed-integer linear program and with the one resulting from the implementation of the optimal strategies in a high-fidelity nonlinear simulator. We showcase the effectiveness of the presented algorithm by assessing the impact of different powertrain configurations and electric motor size on the achievable fuel consumption. Our numerical results show that the proposed algorithm can assess fuel-optimal control strategies with low computational burden, and that powertrain design choices significantly affect the achievable fuel consumption of the vehicle.
  • Salazar Villalon, Mauro; Rossi, Federico; Schiffer, Maximilian; et al. (2018)
    2018 21st International Conference on Intelligent Transportation Systems (ITSC)
    In this paper we study models and coordination policies for intermodal Autonomous Mobility-on-Demand (AMoD), wherein a fleet of self-driving vehicles provides ondemand mobility jointly with public transit. Specifically, we first present a network flow model for intermodal AMoD, where we capture the coupling between AMoD and public transit and the goal is to maximize social welfare. Second, leveraging such a model, we design a pricing and tolling scheme that allows to achieve the social optimum under the assumption of a perfect market with selfish agents. Finally, we present a real-world case study for New York City. Our results show that the coordination between AMoD fleets and public transit can yield significant benefits compared to an AMoD system operating in isolation.
  • Salazar Villalon, Mauro; Balerna, Camillo; Chisari, Eugenio; et al. (2018)
    2018 IEEE Conference on Decision and Control (CDC)
    The powertrain of the Formula 1 car is composed of an electrically turbocharged internal combustion engine and an electric motor used for boosting and regenerative braking. The energy management system that controls this hybrid electric power unit strongly influences the achievable lap time, as well as the fuel and battery consumption. Therefore, it is important to design robust feedback control algorithms that can run on the ECU in compliance with the sporting regulations, and are able to follow lap time optimal strategies while properly reacting to external disturbances. In this paper, we design feedback control algorithms inspired by equivalent consumption minimization strategies (ECMS) that adapt the optimal control policy implemented on the car in real-time. This way, we are able to track energy management strategies computed offline in a lap time optimal way using three PID controllers. We validate the presented control structure with numerical simulations and compare it to a previously designed model predictive control scheme.
  • Salazar Villalon, Mauro; Duhr, Pol; Balerna, Camillo; et al. (2019)
    IEEE Transactions on Vehicular Technology
  • Ritzmann, Johannes; Christon, Andreas; Salazar Villalon, Mauro; et al. (2019)
    SAE Technical Papers
Publications 1 - 7 of 7