arXiv subjects

Mauro Salazar

Publications and source records attributed to Mauro Salazar.

At least 55 records · Page 3Linked to original sources

arXiv2023

Congestion-aware Ride-pooling in Mixed Traffic for Autonomous Mobility-on-Demand Systems

This paper presents a modeling and optimization framework to study congestion-aware ride-pooling Autonomous Mobility-on-Demand (AMoD) systems, whereby self-driving robotaxis are providing on-demand mobility, and users headed in the same direction share the same vehicle for part of their journey. Specifically, taking a mesoscopic time-invariant perspective and on the assumption of a large number of travel requests, we first cast the joint ride-pooling assignment and routing problem as a quadratic program that does not scale with the number of demands and can be solved with off-the-shelf convex solvers. Second, we compare the proposed approach with a significantly simpler decoupled formulation, whereby only the routing is performed in a congestion-aware fashion, whilst the ride-pooling assignment part is congestion-unaware. A case study of Sioux Falls reveals that such a simplification does not significantly alter the solution and that the decisive factor is indeed the congestion-aware routing. Finally, we solve the latter problem accounting for the presence of user-centered private vehicle users in a case study of Manhattan, NYC, characterizing the performance of the car-network as a function of AMoD penetration rate and percentage of pooled rides within it. Our results show that AMoD can significantly reduce congestion and travel times, but only if at least 40% of the users are willing to be pooled together. Otherwise, for higher AMoD penetration rates and low percentage of pooled rides, the effect of the additional rebalancing empty-vehicle trips can be even more detrimental than the benefits stemming from a centralized routing, worsening congestion and leading to an up to 15% higher average travel time.

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arXiv2023

Joint Optimization of Charging Infrastructure Placement and Operational Schedules for a Fleet of Battery Electric Trucks

This paper examines the challenges and requirements for transitioning logistic distribution networks to electric fleets. To maintain their current operations, fleet operators need a clear understanding of the charging infrastructure required and its relationship to existing power grid limitations and fleet schedules. In this context, this paper presents a modeling framework to optimize the charging infrastructure and charging schedules for a logistic distribution network in a joint fashion. Specifically, we cast the joint infrastructure design and operational scheduling problem as a mixed-integer linear program that can be solved with off-the-shelf optimization algorithms providing global optimality guarantees. For a case study in the Netherlands, we assess the impact of different parameters in our optimization problem, specifically, the allowed deviation from existing operations with conventional diesel trucks and the cost factor for daily peak energy usage. We examine the effects on infrastructure design and power requirements, comparing our co-design algorithm with planned infrastructure solutions. The results indicate that current charging and electric machine technologies for trucks can perform the itineraries of conventional trucks for our case study, but to maintain critical time requirements and navigate grid congestion co-design can have a significant impact in reducing total cost of ownership (average 3.51% decrease in total costs compared to rule-based design solutions).

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arXiv2023

Electric Autonomous Mobility-on-Demand: Jointly Optimal Vehicle Design and Fleet Operation

The advent of autonomous driving and electrification is enabling the deployment of Electric Autonomous Mobility-on-Demand (E-AMoD) systems, whereby electric autonomous vehicles provide on-demand mobility. Crucially, the design of the individual vehicles and the fleet, and the operation of the system are strongly coupled. Hence, to maximize the system-level performance, they must be optimized in a joint fashion. To this end, this paper presents a framework to jointly optimize the fleet design in terms of battery capacity and number of vehicles, and the operational strategies of the E-AMoD system, with the aim of maximizing the operator's total profit. Specifically, we first formulate this joint optimization problem using directed acyclic graphs as a mixed integer linear program, which can be solved using commercial solvers with optimality guarantees. Second, to solve large instances of the problem, we propose a solution algorithm that solves for randomly sampled sub-problems, providing a more conservative solution of the full problem, and devise a heuristic approach to tackle larger individual sub-problem instances. Finally, we showcase our framework on a real-world case study in Manhattan, where we demonstrate the interdependence between the number of vehicles, their battery size, and operational and fixed costs. Our results indicate that to maximize a mobility operator's profit, a fleet of small and light vehicles with battery capacity of 20 kWh only can strike the best trade-off in terms of battery degradation, fixed costs and operational efficiency.

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arXiv2023

Electric Aircraft Assignment, Routing, and Charge Scheduling Considering the Availability of Renewable Energy

Electric airplanes are expected to take to the skies soon, finding first use cases in small networks within hardly accessible areas, such as island communities. In this context, the environmental footprint of such airplanes will be strongly determined by the energy sources employed when charging them. This paper presents a framework to optimize aircraft assignment, routing and charge schedules explicitly accounting for the energy availability at the different airports, which are assumed to be equipped with renewable energy sources and stationary batteries. Specifically, considering the daily travel demand and weather conditions forecast in advance, we first capture the aircraft operations within a time-expanded directed acyclic graph, and combine it with a dynamic energy model of the individual airports. Second, aiming at minimizing grid-dependency, we leverage our models to frame the optimal electric aircraft and airport operational problem as a mixed-integer linear program that can be solved with global optimality guarantees. Finally, we showcase our framework in a real-world case-study considering one week of operations on the Dutch Leeward Antilles. Our results show that, depending on weather conditions and compared to current schedules, optimizing flights and operations in a renewable-energy-aware manner can reduce grid dependency from 18 to 100%, whilst significantly shrinking the operational window of the airplanes.

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arXiv2023

Effective Scaling of High-Fidelity Electric Motor Models for Electric Powertrain Design Optimization

In general, electric motor design procedures for automotive applications go through expensive trial-and-error processes or use simplified models that linearly stretch the efficiency map. In this paper, we explore the possibility of efficiently optimizing the motor design directly, using high-fidelity simulation software and derivative-free optimization solvers. In particular, we proportionally scale an already existing electric motor design in axial and radial direction, as well as the sizes of the magnets and slots separately, in commercial motor design software. We encapsulate this motor model in a vehicle model together with the transmission, simulate a candidate design on a drive cycle, and find an optimum through a Bayesian optimization solver. We showcase our framework on a small city car, and observe an energy consumption reduction of 0.13% with respect to a completely proportional scaling method, with a motor that is equipped with relatively shorter but wider magnets and slots. In the extended version of this paper, we include a comparison with the linear models, and add experiments on different drive cycles and vehicle types.

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arXiv2023

A Two-dimensional Spatial Optimization Framework for Vehicle Powertrain Systems

This paper presents a modeling framework to optimize the two-dimensional placement of powertrain elements inside the vehicle, explicitly accounting for the rotation, relative placement and alignment. Specifically, we first capture the multi-level nature of the system mathematically, and construct a model that captures different powertrain component orientations. Second, we include the relative element placement as variables in the model and derive alignment constraints for both child components and parent subsystems to automatically connect mechanical ports. Finally, we showcase our framework on a four-wheel driven electric vehicle. Our results demonstrate that our framework is capable of efficiently generating system design solutions in a fully automated manner, only using basic component properties.

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arXiv2023

Cost-optimal Fleet Management Strategies for Solar-electric Autonomous Mobility-on-Demand Systems

This paper studies mobility systems that incorporate a substantial solar energy component, generated not only on the ground, but also through solar roofs installed on vehicles, directly covering a portion of their energy consumption. In particular, we focus on Solar-electric Autonomous Mobility-on-Demand systems, whereby solar-electric autonomous vehicles provide on-demand mobility, and optimize their operation in terms of serving passenger requests, charging and vehicle-to-grid (V2G) operations. We model this fleet management problem via directed acyclic graphs and parse it as a mixed-integer linear program that can be solved using off-the-shelf solvers. We showcase our framework in a case study of Gold Coast, Australia, analyzing the fleet's optimal operation while accounting for electricity price fluctuations resulting from a significant integration of solar power in the total energy mix. We demonstrate that using a solar-electric fleet can reduce the total cost of operation of the fleet by 10-15% compared to an electric-only counterpart. Finally, we show that for V2G operations using vehicles with a larger battery size can significantly lower the operational costs of the fleet, overcompensating its higher energy consumption by trading larger volumes of energy and even accruing profits.

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arXiv2023

A Data-driven Pricing Scheme for Optimal Routing through Artificial Currencies

Mobility systems often suffer from a high price of anarchy due to the uncontrolled behavior of selfish users. This may result in societal costs that are significantly higher compared to what could be achieved by a centralized system-optimal controller. Monetary tolling schemes can effectively align the behavior of selfish users with the system-optimum. Yet, they inevitably discriminate the population in terms of income. Artificial currencies were recently presented as an effective alternative that can achieve the same performance, whilst guaranteeing fairness among the population. However, those studies were based on behavioral models that may differ from practical implementations. This paper presents a data-driven approach to automatically adapt artificial-currency tolls within repetitive-game settings. We first consider a parallel-arc setting whereby users commute on a daily basis from an individual origin to an individual destination, choosing a route in exchange of an artificial-currency price or reward, while accounting for the impact of the choices of the other users on travel discomfort. Second, we devise a model-based reinforcement learning controller that autonomously learns the optimal pricing policy by interacting with the proposed framework considering the closeness of the observed aggregate flows to a desired system-optimal distribution as a reward function. Our numerical results show that the proposed data-driven pricing scheme can effectively align the users' flows with the system optimum, significantly reducing the societal costs with respect to the uncontrolled flows (by about 15% and 25% depending on the scenario), and respond to environmental changes in a robust and efficient manner.

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arXiv2023

Urgency-aware Routing in Single Origin-destination Itineraries through Artificial Currencies

Within mobility systems, the presence of self-interested users can lead to aggregate routing patterns that are far from the societal optimum which could be achieved by centrally controlling the users' choices. In this paper, we design a fair incentive mechanism to steer the selfish behavior of the users to align with the societally optimal aggregate routing. The proposed mechanism is based on an artificial currency that cannot be traded or bought, but only spent or received when traveling. Specifically, we consider a parallel-arc network with a single origin and destination node within a repeated game setting whereby each user chooses from one of the available arcs to reach their destination on a daily basis. In this framework, taking faster routes comes at a cost, whereas taking slower routes is incentivized by a reward. The users are thus playing against their future selves when choosing their present actions. To capture this complex behavior, we assume the users to be rational and to minimize an urgency-weighted combination of their immediate and future discomfort. To design the optimal pricing, we first derive a closed-form expression for the best individual response strategy. Second, we formulate the pricing design problem for each arc to achieve the societally optimal aggregate flows, and reformulate it so that it can be solved with gradient-free optimization methods. Our numerical simulations show that it is possible to achieve a near-optimal routing whilst significantly reducing the users' perceived discomfort when compared to a centralized optimal but urgency-unaware policy.

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arXiv2023

A Time-invariant Network Flow Model for Two-person Ride-pooling Mobility-on-Demand

This paper presents a time-invariant network flow model capturing two-person ride-pooling that can be integrated within design and planning frameworks for Mobility-on-Demand systems. In these type of models, the arrival process of travel requests is described by a Poisson process, meaning that there is only statistical insight into request times, including the probability that two requests may be pooled together. Taking advantage of this feature, we devise a method to capture ride-pooling from a stochastic mesoscopic perspective. This way, we are able to transform the original set of requests into an equivalent set including pooled ones which can be integrated within standard network flow problems, which in turn can be efficiently solved with off-the-shelf LP solvers for a given ride-pooling request assignment. Thereby, to compute such an assignment, we devise a polynomial-time algorithm that is optimal w.r.t. an approximated version of the problem. Finally, we perform a case study of Sioux Falls, South Dakota, USA, where we quantify the effects that waiting time and experienced delay have on the vehicle-hours traveled. Our results suggest that the higher the demands per unit time, the lower the waiting time and delay experienced by users. In addition, for a sufficiently large number of demands per unit time, with a maximum waiting time and experienced delay of 5 minutes, more than 90% of the requests can be pooled.

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arXiv2022

Electric Autonomous Mobility-on-Demand: Joint Optimization of Routing and Charging Infrastructure Siting

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.

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arXiv2022

Electric Motor Design Optimization: A Convex Surrogate Modeling Approach

This paper instantiates a convex electric powertrain design optimization framework, bridging the gap between high-level powertrain sizing and low-level components design. We focus on the electric motor and transmission of electric vehicles, using a scalable convex motor model based on surrogate modeling techniques. Specifically, we first select relevant motor design variables and evaluate high-fidelity samples according to a predefined sampling plan. Second, using the sample data, we identify a convex model of the motor, which predicts its losses as a function of the operating point and the design parameters. We also identify models of the remaining components of the powertrain, namely a battery and a fixed-gear transmission. Third, we frame the minimum-energy consumption design problem over a drive cycle as a second-order conic program that can be efficiently solved with optimality guarantees. Finally, we showcase our framework in a case study for a compact family car and compute the optimal motor design and transmission ratio. We validate the accuracy of our models with a high-fidelity simulation tool and calculate the drift in battery energy consumption. We show that our model can capture the optimal operating line and the error in battery energy consumption is low. Overall, our framework can provide electric motor design experts with useful starting points for further design optimization.

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arXiv2022

Control and Design Optimization of an Electric Vehicle Transmission Using Analytical Modeling Methods

This paper introduces a framework to systematically optimize the control and design of an electric vehicle transmission, connecting powertrain sizing studies to detailed gearbox design methods. To this end, we first create analytical models of individual components: gears, shafts, bearings, clutches, and synchronizers. Second, we construct a transmission by systematically configuring a topology with these components. Third, we place the composed transmission within a powertrain and vehicle model, and compute the minimum-energy control and design, employing solving algorithms that provide global optimality guarantees. Finally, we carry out the control and design optimization of a fixed- and two-gear transmission for a compact family electric vehicle, whereby we observe that a two-gear transmission can improve the energy consumption by 0.8 %, while also achieving requirements on gradeability and performance. We validate our framework by recreating the transmission that is mounted in the benchmark test case vehicle and recomputing the energy consumption over the New European Driving Cycle, where we notice an error in energy consumption of 0.2 %, affirming that our methods are suitable for gear ratio selection in powertrain design optimization.

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arXiv2022

Joint Routing and Charging Problem of Electric Vehicles with Incentive-aware Customers Considering Spatio-temporal Charging Prices

This paper investigates the scheduling problem of a fleet of electric vehicles, providing mobility as a service to a set of time-specified customers, where the operator needs to solve the routing and charging problem jointly for each EV. Hereby we consider incentive-aware customers and propose that the operator offers monetary incentives to customers in exchange for time flexibility. In this way, the fleet operator can achieve a routing and charging schedule with lower costs, whilst the customers receive monetary compensation for their flexibility. Specifically, we first propose a bi-level optimization model whereby the fleet operator optimizes the routing and charging schedule accounting for the spatio-temporal varying charging price, jointly with a monetary incentive to reimburse the delivery time flexibility experienced by the customers. Concurrently the customers choose their own time flexibility by minimizing their own cost. Second, we cope with the computational burden coming from this nonlinear bi-level optimization model with an accurate reformulation approach consisting of the KKT optimality conditions, a Big-M-based linearization method, and the zero duality gap of convex optimization problems. This way, we convert the proposed problem into a single-level optimization problem, which can be solved by a strengthened generalized Benders decomposition method holding a faster convergence rate than the generalized Benders decomposition method. To evaluate the effectiveness of the proposed mathematical model, we carry out numerous simulation experiments by using the VRP-REP data of Belgium. The numerical results showcase that the proposed mathematical model can reduce the delivery fees for the customers together with the cost of operation incurred by the fleet operator.

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arXiv2022

Maximum-distance Race Strategies for a Fully Electric Endurance Race Car

This paper presents a bi-level optimization framework to compute the maximum-distance stint and charging strategies for a fully electric endurance race car. Thereby, the lower level computes the minimum-stint-time Powertrain Operation (PO) for a given battery energy budget and stint length, whilst the upper level leverages that information to jointly optimize the stint length, charge time and number of pit stops, in order to maximize the driven distance in the course of a fixed-time endurance race. Specifically, we first extend a convex lap time optimization framework to capture multiple laps and force-based electric motor models, and use it to create a map linking the charge time and stint length to the achievable stint time. Second, we leverage the map to frame the maximum-race-distance problem as a mixed-integer second order conic program that can be efficiently solved to the global optimum with off-the-shelf optimization algorithms. Finally, we showcase our framework on a 6 h race around the Zandvoort circuit. Our results show that a flat-out strategy can be extremely detrimental, and that, compared to when the stints are optimized for a fixed number of pit stops, jointly optimizing the stints and number of pit stops can increase the driven distance of several laps.

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arXiv2022

Minimum-lap-time Control Strategies for All-wheel Drive Electric Race Cars via Convex Optimization

This paper presents a convex optimization framework to compute the minimum-lap-time control strategies of all-wheel drive (AWD) battery electric race cars, accounting for the grip limitations of the individual tyres. Specifically, we first derive the equations of motion (EOM) of the race car and simplify them to a convex form. Second, we leverage convex models of the electric motors (EMs) and battery, and frame the time-optimal final-drives design and EMs control problem in space domain. The resulting optimization problem is fully convex and can be efficiently solved with global optimality guarantees using second-order conic programming algorithms. Finally, we validate our modeling assumptions via the original non-convex EOM, and simulate our framework on the Formula Student Netherlands endurance race track. Thereby, we compare a torque vectoring with a fixed power split configuration, showing that via torque vectoring we can make a better use of the individual tyre grip, and significantly improve the achievable lap time by more than 4%. Finally, we present a design study investigating the respective impact of the front and rear EM size on lap time, revealing that the rear motor sizing is predominant due to the higher vertical rear tyre load caused by the center of pressure position and rearwards load transfer under acceleration.

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arXiv2022

Energy-optimal Design and Control of Electric Powertrains under Motor Thermal Constraints

This paper presents a modeling and optimization framework to minimize the energy consumption of a fully electric powertrain by optimizing its design and control strategies whilst explicitly accounting for the thermal behavior of the Electric Motor (EM). Specifically, we first derive convex models of the powertrain components, including the battery, the EM, the transmission and a Lumped Parameter Thermal Network (LPTN) capturing the thermal dynamics of the EM. Second, we frame the optimal control problem in time domain, and devise a two-step algorithm to accelerate convergence and efficiently solve the resulting convex problem via nonlinear programming. Subsequently, we present a case study for a compact family car, optimize its transmission design and operation jointly with the regenerative braking and EM cooling control strategies for a finite number of motors and transmission technologies. We validate our proposed models using the high-fidelity simulation software Motor-CAD, showing that the LPTN quite accurately captures the thermal dynamics of the EM, and that the permanent magnets' temperature is the limiting factor during extended driving. Furthermore, our results reveal that powertrains equipped with a continuously variable transmission (CVT) result into a lower energy consumption than with a fixed-gear transmission (FGT), as a CVT can lower the EM losses, resulting in lower EM temperatures. Finally, our results emphasize the significance of considering the thermal behavior when designing an EM and the potential offered by CVTs in terms of downsizing.

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arXiv2022

Optimizing Vaccine Allocation Strategies in Pandemic Outbreaks: An Optimal Control Approach

Since early 2020, the world has been dealing with a raging pandemic outbreak: COVID-19. A year later, vaccines have become accessible, but in limited quantities, so that governments needed to devise a strategy to decide which part of the population to prioritize when assigning the available doses, and how to manage the interval between doses for multi-dose vaccines. In this paper, we present an optimization framework to address the dynamic double-dose vaccine allocation problem whereby the available vaccine doses must be administered to different age-groups to minimize specific societal objectives. In particular, we first identify an age-dependent Susceptible-Exposed-Infected-Recovered (SEIR) epidemic model including an extension capturing partially and fully vaccinated people, whereby we account for age-dependent immunity and infectiousness levels together with disease severity. Second, we leverage our model to frame the dynamic age-dependent vaccine allocation problem for different societal objectives, such as the minimization of infections or fatalities, and solve it with nonlinear programming techniques. Finally, we carry out a numerical case study with real-world data from The Netherlands. Our results show how different societal objectives can significantly alter the optimal vaccine allocation strategy. For instance, we find that minimizing the overall number of infections results in delaying second doses, whilst to minimize fatalities it is important to fully vaccinate the elderly first.

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