arXiv subjects

Mauro Salazar

Publications and source records attributed to Mauro Salazar.

At least 19 recordsLinked to original sources

arXiv2026

An Optimal Power Management Policy for Hydrogen-based Hybrid Aero Engines

This paper presents a power management policy for a hydrogen-based hybrid aero engine combining a gas turbine and a solid oxide fuel cell (SOFC). Specifically, we first identify a quadratic quasi-steady-state model of the propulsion system and formulate the minimum-fuel optimal control problem as a function of the power split between gas turbine and SOFC that captures the interconnections between the components and accounts for their operational limits. Second, leveraging the Karush-Kuhn-Tucker optimality conditions and partial convexity and monotonicity model properties, we compute the globally optimal steady-state power split for the different phases of the flight in closed form. Finally, we verify this power management policy with a high-fidelity integrated static model %simulator across different flight phases, revealing in less than 1.5 % normalized root mean square error in power allocation and less than 0.7 % in predicted fuel consumption. Our results show that the optimal power management policy can be translated into a heuristic control law requesting the highest SOFC power that does not exceed its maximum operating temperature, ultimately paving the way for minimal-effort on-board implementations.

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arXiv2026

Optimization Models and Steady-State Minimum-Fuel Operating Strategies for Hydrogen-based Hybrid Electric Aerospace Propulsion Systems

This paper presents an optimization framework for the operation of hydrogen-based hybrid electric aerospace propulsion systems consisting of a hydrogen gas turbine and an electric motor powered by a solid oxide fuel cell, connected to the gas turbine via multiple gas channels and heat exchangers. Our framework computes the minimum-fuel optimal operating strategies over a flight mission accounting for the complex propulsion system with strong thermodynamic and mechanical coupling between components. First, we identify surrogate optimization models of the components employing high-fidelity model simulations. Second, we frame the minimum-fuel optimal control problem over a given flight mission and parse it into a static nonlinear optimization problem that can be efficiently solved with off-the-shelf nonlinear programming algorithms. Finally, we apply our optimization framework to a typical flight mission of an advanced, commuter aircraft (Beechcraft 1900D market segment), considering a parallel propulsion system architecture with four different configurations that share a common baseline but differ in the inclusion of an additional battery and by-pass valves around the two heat exchangers. The resulting optimal trajectories are validated against high-fidelity simulation results, demonstrating the accuracy of our framework. Results show that adding by-pass valves around the air and hydrogen heat exchangers can reduce fuel consumption by 19.11 % without the battery, and by 19.56% with the battery. We show that adding a battery yields a slight increase in fuel consumption (below 1%) for future projected energy densities under steady-state conditions. Conversely, when considering state-of-the art energy densities, the additional battery weight outweighs the benefits, limiting its potential applicability to only assisting transients, which are not considered in the present work.

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arXiv2026

Justice-informed Planning of Intermodal Autonomous Mobility-on-Demand Systems under Operational Constraints

To date, most of the research on transport planning has focused on optimizing revenues or utilitarian metrics such as average travel times, which often ends up penalizing the worst-off for the sake of profit or efficiency. At the same time, most of the research in transport justice has focused on assessing injustices, without being able to prescribe operational solutions. This paper contributes to bridging this gap and presents optimization models for justice-informed operational planning of intermodal mobility systems that explicitly account for the budget and safety limitations of users, and for infrastructural capacity constraints. Specifically, we first focus on an intermodal Autonomous Mobility-on-Demand (AMoD) system -- where self-driving robotaxis provide on-demand mobility jointly with public transit and active modes -- and characterize its operations from a mesoscopic planning perspective via network flow models. Second, we leverage these models to optimize system operations through both utilitarian efficiency and justice-informed objectives. We showcase our framework in a real-world case-study for Manhattan, New York. Our results show that monetary budgets significantly limit the social justice potential of AMoD systems if they are to be deployed as transportation network companies. At the same time, granting free public transit can result in sufficiency levels very close to a completely free intermodal AMoD system, where justice-informed operations can be achieved without compromising standard efficiency metrics, ultimately highlighting the strong potential of social policies.

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arXiv2026

Battery Electric Truck Infrastructure Co-design via Joint Optimization and Agent-based Simulation

As zero-emission zones emerge in European cities, fleet operators are shifting to electric vehicles. To maintain their current operations, a clear understanding of the charging infrastructure required and its relationship to existing power grid limitations is needed. This study presents an optimization frame-work for jointly designing charging infrastructure and schedules within a logistics distribution network, validated through agent-based simulations. We formulate the problem as a mixed-integer linear program and develop an agent-based model to evaluate various designs and operations under stochastic conditions. Our experiments compare rule-based and optimized strategies in a case study of the Netherlands. Results show that current commercial solutions suffice for middle-mile logistics, with central co-design yielding average cost reductions of 5.2% to 6.4% and an average 20.1% decrease in total installed power. While rule-based control effectively manages charging operations and mitigates delays, optimizing charge scheduling significantly reduces queuing times (99%), charging costs (13.5%), and time spent near capacity (10.9%). Our optimization-simulation framework paves the way for combining optimized infrastructure planning and realistic fleet operations in digital-twin environments.

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arXiv2026

Simultaneous Optimization of Electric Ferry Operations and Charging Infrastructure

Electrification of marine transport is a promising solution to reduce sector greenhouse gas emissions and operational costs. However, the large upfront cost of electric vessels and the required charging infrastructure can be a barrier to the development of this technology. Optimization algorithms that jointly design the charging infrastructure and the operation of electric vessels can help to reduce these costs and make these projects viable. In this paper, we present a mixed-integer linear programming optimization framework that jointly schedules ferry operations, charging infrastructure and ship battery size. We analyze our algorithms with the case of the China Zorrilla, the largest electric ferry in the world, which will operate between Buenos Aires and Colonia del Sacramento in 2025. We find that the joint system and operations design can reduce the total costs by 7.8\% compared to a scenario with fixed power limits and no port energy management system.

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arXiv2026

Competitor-aware Race Management for Electric Endurance Racing

Electric endurance racing is characterized by severe energy constraints and strong aerodynamic interactions. Determining race-winning policies therefore becomes a fundamentally multi-agent, game-theoretic problem. These policies must jointly govern low-level driver inputs as well as high-level strategic decisions, including energy management and charging. This paper proposes a bi-level framework for competitor-aware race management that combines game-theoretic optimal control with reinforcement learning. At the lower level, a multi-agent game-theoretic optimal control problem is solved to capture aerodynamic effects and asymmetric collision-avoidance constraints inspired by motorsport rules. Using this single-lap problem as the environment, reinforcement learning agents are trained to allocate battery energy and schedule pit stops over an entire race. The framework is demonstrated in a two-agent, 45-lap simulated race. The results show that effective exploitation of aerodynamic interactions is decisive for race outcome, with strategies that prioritize finishing position differing fundamentally from single-agent, minimum-time approaches.

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arXiv2026

Token Economy for Fair and Efficient Dynamic Resource Allocation in Congestion Games

Self-interested behavior in sharing economies often leads to inefficient aggregate outcomes compared to a centrally coordinated allocation, ultimately harming users. Yet, centralized coordination removes individual decision power. This issue can be addressed by designing rules that align individual preferences with system-level objectives. Unfortunately, rules based on conventional monetary mechanisms introduce unfairness by discriminating among users based on their wealth. To solve this problem, in this paper, we propose a token-based mechanism for congestion games that achieves efficient and fair dynamic resource allocation. Specifically, we model the token economy as a continuous-time dynamic game with finitely many boundedly rational agents, explicitly capturing their evolutionary policy-revision dynamics. We derive a mean-field approximation of the finite-population game and establish strong approximation guarantees between the mean-field and the finite-population games. This approximation enables the design of integer tolls in closed form that provably steer the aggregate dynamics toward an optimal efficient and fair allocation from any initial condition.

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arXiv2026

A Computationally Efficient and Human Implementable Minimum-lap-time Control Policy for Energy-limited Race Cars

This paper presents a provably optimal, real-time capable energy management policy for race cars that provides simple human-driver-implementable control cues. Specifically, we first formulate the energy-constrained minimum-lap-time control problem via Pontryagin's Minimum Principle (PMP) and derive the optimal policy and costate dynamics using Karush-Kuhn-Tucker (KKT) optimality conditions. We show that the optimal control policy follows a bang-bang structure that is easily implementable by a human driver, eliminating the need for potentially dangerous active throttle pedal overwrites or distracting signals. Moreover, the analytical formulation of the optimal system dynamics allows us to recast the problem as a sequence of boundary-value problems, which can be efficiently solved using root-finding methods. Our results show that our proposed approach can compute the same globally optimal control strategies of existing numerical methods based on direct optimal control, whilst drastically reducing computation time from the order of seconds to milliseconds.

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arXiv2026

Energy Management Strategies for Electric Aircraft Charging Leveraging Active Landside Vehicle-to-Grid

The deployment of medium-range battery electric aircraft is a promising pathway to improve the environmental footprint of air mobility. Yet such a deployment would be accompanied by significant electric power requirements at airports due to aircraft charging. Given the growing prevalence of electric vehicles and their bi-directional charging capabilities--so-called vehicle-to-grid (V2G)--we study energy buffer capabilities of parked electric vehicles to alleviate pressure on grid connections. To this end, we present energy management strategies for airports providing cost-optimal apron and landside V2G charge scheduling. Specifically, we first formulate the optimal energy management problem of joint aircraft charging and landside V2G coordination as a linear program, whereby we use partial differential equations to model the aggregated charging dynamics of the electric vehicle fleet. Second, we consider a shuttle flight network with a single hub of a large Dutch airline, real-world grid prices, and synthetic parking garage occupancy data to test our framework. Our results show that V2G at even a single airport can indeed reduce energy costs to charge the aircraft fleet: Compared to a baseline scenario without V2G, the proposed concept yields cost savings of up to 32%, depending on the schedule and amount of participating vehicles, and has other potential beneficial effects on the local power grid, e.g., the reduction of potential power peaks.

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arXiv2025

Two-dimensional Spatial Optimization for Electric Motorcycle Powertrain Elements using Mixed-integer Programming

This study presents a framework for optimizing the two-dimensional (2D) placement of electric motorcycle powertrain elements, accounting for the position, the orientation and geometric irregularities. Specifically, we construct a 2D placement model at the component level in which we include near-continuous rotation of components and allow for irregular subsystem geometries to make optimal use of the limited design space. Second, we introduce linearization techniques for the trigonometric constraints and formulate the placement problem as a mixed-integer quadratic program (MIQP). Finally, we demonstrate our framework on two electric motorcycle powertrain topologies and study the influence of the geometry complexity on the placement solutions. The results show that gradually increasing complexity leads to more manageable computation times and higher the complexity solution improves handling performance by 2.5% compared to the benchmark placement found in existing electric motorcycles.

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arXiv2025

A Sequential Convex Programming Approach to Free-trajectory Minimum-lap-time Optimization of Racing Cars

This paper presents a modeling and optimization framework to compute the minimum-lap-time spatial trajectory and powertrain operation of racing cars in a computationally efficient fashion. Specifically, we first derive a quasi-steady-state model of a racing car, whereby the racing line trajectory is jointly optimized. Next, we frame the minimum-lap-time problem and leverage its mostly convex structure by devising a sequential convex programming solution algorithm. We benchmark our method against off-the-shelf nonlinear programming solvers, showing how it can bring computation time down from a few minutes to a few seconds, paving the way for real-time implementations. Moreover, we compare our results to similarly efficient minimum-curvature racing line optimization methods, showing how a minimum-time-based racing line might lead to 4% faster lap-times. Finally, we showcase our framework for optimal powertrain energy management and we validate the common modeling assumption that the racing line is unaffected by energy limitations, showing that this assumption results in marginal lap-time losses of under 0.1%.

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arXiv2025

Evolutionary Analysis of Continuous-time Finite-state Mean Field Games with Discounted Payoffs

We consider a class of continuous-time dynamic games involving a large number of players. Each player selects actions from a finite set and evolves through a finite set of states. State transitions occur stochastically and depend on the player's chosen action. A player's single-stage reward depends on their state, action, and the population-wide distribution of states and actions, capturing aggregate effects such as congestion in traffic networks. Each player seeks to maximize a discounted infinite-horizon reward. Existing evolutionary game-theoretic approaches introduce a model for the way individual players update their decisions in static environments without individual state dynamics. In contrast, this work develops an evolutionary framework for dynamic games with explicit state evolution, which is necessary to model many applications. We introduce a mean field approximation of the finite-population game and establish approximation guarantees. Since state-of-the-art solution concepts for dynamic games lack an evolutionary interpretation, we propose a new concept - the Mixed Stationary Nash Equilibrium (MSNE) - which admits one. We characterize an equivalence between MSNE and the rest points of the proposed mean field evolutionary model and we give conditions for the evolutionary stability of MSNE.

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arXiv2025

Evolutionary Dynamics in Continuous-time Finite-state Mean Field Games -- Part II: Stability

We study a dynamic game with a large population of players who choose actions from a finite set in continuous time. Each player has a state in a finite state space that evolves stochastically with their actions. A player's reward depends not only on their own state and action but also on the distribution of states and actions across the population, capturing effects such as congestion in traffic networks. In Part I, we introduced an evolutionary model and a new solution concept - the mixed stationary Nash Equilibrium (MSNE) - which coincides with the rest points of the mean field evolutionary model under meaningful families of revision protocols. In this second part, we investigate the evolutionary stability of MSNE. We derive conditions on both the structure of the MSNE and the game's payoff map that ensure local and global stability under evolutionary dynamics. These results characterize when MSNE can robustly emerge and persist against strategic deviations, thereby providing insight into its long-term viability in large population dynamic games.

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arXiv2025

Evolutionary Dynamics in Continuous-time Finite-state Mean Field Games -- Part I: Equilibria

We study a dynamic game with a large population of players who choose actions from a finite set in continuous time. Each player has a state in a finite state space that evolves stochastically with their actions. A player's reward depends not only on their own state and action but also on the distribution of states and actions across the population, capturing effects such as congestion in traffic networks. While prior work in evolutionary game theory has primarily focused on static games without individual player state dynamics, we present the first comprehensive evolutionary analysis of such dynamic games. We propose an evolutionary model together with a mean field approximation of the finite-population game and establish strong approximation guarantees. We show that standard solution concepts for dynamic games lack an evolutionary interpretation, and we propose a new concept - the Mixed Stationary Nash Equilibrium (MSNE) - which admits one. We analyze the relationship between MSNE and the rest points of the mean field evolutionary model and study the evolutionary stability of MSNE.

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arXiv2025

A Scenario-based Model Predictive Control Scheme for Pandemic Response through Non-pharmaceutical Interventions

This paper presents a scenario-based model predictive control (MPC) scheme designed to control an evolving pandemic via non-pharmaceutical intervention (NPIs). The proposed approach combines predictions of possible pandemic evolution to decide on a level of severity of NPIs to be implemented over multiple weeks to maintain hospital pressure below a prescribed threshold, while minimizing their impact on society. Specifically, we first introduce a compartmental model which divides the population into Susceptible, Infected, Detected, Threatened, Healed, and Expired (SIDTHE) subpopulations and describe its positive invariant set. This model is expressive enough to explicitly capture the fraction of hospitalized individuals while preserving parameter identifiability w.r.t. publicly available datasets. Second, we devise a scenario-based MPC scheme with recourse actions that captures potential uncertainty of the model parameters. e.g., due to population behavior or seasonality. Our results show that the scenario-based nature of the proposed controller manages to adequately respond to all scenarios, keeping the hospital pressure at bay also in very challenging situations when conventional MPC methods fail.

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arXiv2025

Meta-analysis of Life Cycle Assessments for Li-Ion Batteries Production Emissions

This paper investigates the environmental impact of Li-Ion batteries by quantifying manufacturing-related emissions and analyzing how electricity mix and production scale affect emission intensity. To this end, we conduct a meta-analysis of life cycle assessments on lithium-ion batteries published over the past two decades, categorizing them by year, battery chemistry, functional unit, system boundaries, and electricity mix. We then carry out a cradle-to-gate assessment for a nickel manganese cobalt 811 battery with a silicon-coated graphite anode, analyzing how variations in the carbon intensity of the electricity mix affect emissions, with case studies for China, South Korea, and Sweden. Finally, we develop a set of regression models that link annual battery production and the carbon intensity of China's electricity mix to the average mass-specific emissions observed each year. The meta-analysis shows a median global warming potential of 17.63 kg CO2-eq./kg of battery, with a standard deviation of 7.34. Differences in electricity mix mainly influence emissions from the energy-intensive cell production, particularly from cathode material processing. We found that a multivariate linear regression using production volume and the carbon intensity of the Chinese electricity mix as predictors explains emissions with moderate accuracy. The environmental impact of battery manufacturing can be reduced by using clean energy sources in production processes. However, achieving substantial reductions requires clean energy throughout the entire supply chain, as importing materials from regions with carbon-intensive electricity mixes can undermine these efforts. Our findings also highlight the emission-reducing effect of learning associated with increased production scale, supporting the integration of learning effects in future life cycle assessment models.

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arXiv2025

Optimal Co-Design of a Hybrid Energy Storage System for Truck Charging

The major challenges to battery electric truck adoption are their high cost and grid congestion.In this context, stationary energy storage systems can help mitigate both issues. Since their design and operation are strongly coupled, to make the best out of them, they should be jointly optimized. This paper presents a co-design framework for hybrid energy storage systems where their technology and sizing are optimized jointly with their operational strategies. Specifically, we consider a microgrid supporting truck chargers that consists of utility grid, solar panels, and energy storage systems including batteries, supercapacitors and flywheels. We frame the co-design problem as a mixed-integer linear program that can be solved with global optimality guarantees. We showcase our framework in a case-study of a distribution center in the Netherlands. Our results show that although the battery-only configuration is already competitive, adding supercapacitors or flywheel storage decrease total cost and increase energy sold back to the grid. Overall, the fully hybrid solution (Battery+Supercapacitors+Flywheel) offers the best outcomes, achieving the lowest overall cost (1.96\% lower compared to battery-only) and reduced grid dependency, but at a higher (2.6\%) initial investment.

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arXiv2025

Stochastic Model Predictive Control of Charging Energy Hubs with Conformal Prediction

This paper presents an online energy management system for an energy hub where electric vehicles are charged combining on-site photovoltaic generation and battery energy storage with the power grid, with the objective to decide on the battery (dis)charging to minimize the costs of operation. To this end, we devise a scenario-based stochastic model predictive control (MPC) scheme that leverages probabilistic 24-hour-ahead forecasts of charging load, solar generation and day-ahead electricity prices to achieve a cost-optimal operation of the energy hub. The probabilistic forecasts leverage conformal prediction providing calibrated distribution-free confidence intervals starting from a machine learning model that generates no uncertainty quantification. We showcase our controller by running it over a 280-day evaluation in a closed-loop simulated environment to compare the observed cost of two scenario-based MPCs with two deterministic alternatives: a version with point forecast and a version with perfect forecast. Our results indicate that, compared to the perfect forecast implementation, our proposed scenario-based MPCs are 13% more expensive, and 1% better than their deterministic point-forecast counterpart

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