arXiv subjects

Mauro Salazar

Publications and source records attributed to Mauro Salazar.

At least 37 records · Page 2Linked to original sources

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.

eess.SY↗

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.

eess.SY↗

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.

eess.SY↗

arXiv2025

Double-tough ceramics: Optimization-supported multiscale computational design

To overcome the brittleness limitation of ceramics, various toughening mechanisms have been proposed. Some of the most remarkable, especially for oxides, include the tetragonal-to-monoclinic phase transformation leading to crack shielding in zirconia, and bioinspired brick-and-mortar microstructures fostering crack deflection. It has, however, proven challenging to incorporate both these mechanisms into a single all-ceramic material. In this work, we propose a computational methodology for the design of a material that combines these two toughening strategies, using a multiscale modeling approach that captures both their individual contributions and the overall fracture performance. This is achieved by developing an all-ceramic composite with a brick-and-mortar microstructure, in which the nanocrystalline mortar is transformation-toughened. Key factors influencing phase transformation, such as grain boundary properties, grain orientations, and kinetic coefficients, are analyzed, and the resulting transformation stress-strain behavior is incorporated into the microscale mortar constitutive model. We demonstrate that the synergistic effect of the two toughening mechanisms is achievable, and that it is an extremely effective strategy to boost fracture performance. The influence of brick size, mortar thickness, and properties of the constituent materials is then systematically investigated. Finally, a gradient-free optimization algorithm is employed to identify optimal geometric and material parameters, revealing that longer, thinner bricks with minimal mortar thickness provide the best fracture resistance. Optimal combinations of material properties are identified for given brick sizes and mortar thicknesses.

cond-mat.mtrl-sci↗

arXiv2024

Human-in-the-loop Energy and Thermal Management for Electric Racing Cars through Optimization-based Control

This paper presents an energy and thermal management system for electric race cars, where we tune a lift-off-throttle signal for the driver in real-time to respect energy budgets and thermal constraints. First, we compute the globally optimal state trajectories in a real-time capable solving time, optimizing a 47-kilometer horizon in 2.5 seconds. Next, for safe operation with a human driver, we simplify it to a maximum-power-or-coast operation in full-throttle regions (straights). Thereby, both the positions from which the vehicle should start coasting and the optimal throttle map are subject to tuning. To this end, we define the coasting sections with a threshold on the co-state trajectory of the kinetic energy from the optimal solution. We devise an online implementable bisection algorithm to tune this threshold and adapt it using PI feedback. Finally, we validate the proposed approach for an electric endurance race car and compare three variants with varying implementation challenges: one re-optimizing and updating the kinetic co-state trajectory online, one applying only the bisection algorithm online, and one relying exclusively on feedback control. Our results show that, under typical racing disturbances, our energy management can achieve stint times ranging from less than 0.056\% to 0.22\% slower compared to offline optimization with a priori knowledge of disturbances, paving the way for on-board implementations and testing.

math.OC↗

arXiv2024

A Human-optimized Model Predictive Control Scheme and Extremum Seeking Parameter Estimator for Slip Control of Electric Race Cars

This paper presents a longitudinal slip control system for a rear-wheel-driven electric endurance race car. The control system integrates Model Predictive Control (MPC) with Extremum Seeking Control (ESC) to optimize the traction and regenerative braking performance of the powertrain. The MPC contains an analytical solution which results in a negligible computation time, whilst providing an optimal solution to a multi-objective optimization problem. The ESC algorithm allows continuous estimation of the optimal slip reference without assuming any prior knowledge of the tire dynamics. Finally, the control parameters are determined using a human-driven preference-based optimization algorithm in order to obtain the desired response. Simulation results and comparisons with other methods demonstrate the system's capability to automatically determine and track the optimal slip values, showing stability and performance under varying conditions.

eess.SY↗

arXiv2024

Concurrent Design Optimization of Powertrain Component Modules in a Family of Electric Vehicles

We present a modeling and optimization framework to design powertrains for a family of electric vehicles, focusing on the concurrent sizing of their motors and batteries. Whilst tailoring these component modules to each individual vehicle type can minimize energy consumption, it can result in high production costs due to the variety of component modules to be realized for the family of vehicles, driving the Total Costs of Ownership (TCO) high. Against this backdrop, we explore modularity and standardization strategies whereby we jointly design unique motor and battery modules to be installed in all the vehicles in the family, using a different number of these modules when needed. Such an approach results in higher production volumes of the same component module, entailing significantly lower manufacturing costs due to Economy-of-Scale (EoS) effects, and hence a potentially lower TCO for the family of vehicles. To solve the resulting one-size-fits-all problem, we instantiate a nested framework consisting of an inner convex optimization routine which jointly optimizes the modules' sizes and the powertrain operation of the entire family, for given driving cycles and modules' multiplicities. Likewise, we devise an outer loop comparing each configuration to identify the minimum-TCO solution with global optimality guarantees. Finally, we showcase our framework on a case study for the Tesla vehicle family in a benchmark design problem, considering the Model S, Model 3, Model X, and Model Y. Our results show that, compared to an individually tailored design, the application of our concurrent design optimization framework achieves a significant reduction of the production costs for a minimal increase in operational costs, ultimately lowering the family TCO in the benchmark design problem by 3.5\%.

math.OC↗

arXiv2024

A Framework to Estimate Life Cycle Emissions for Vehicle-Integrated Photovoltaic Systems

This paper presents a framework to estimate the environmental impact of solar electric vehicles, accounting for the emissions caused by photovoltaic system production as well as vehicle use. We leverage a cradle-to-gate life cycle assessment to estimate the greenhouse gas emissions of the vehicle-integrated photovoltaic system, from the raw material extraction to the final panel assembly, including the effect of the electricity mix both at the factory location and in the country of use. %the vehicle's life cycle, considering both Furthermore, we modify an existing optimization framework for battery electric vehicles to optimally design a solar electric vehicle and estimate its energy consumption. We showcase our framework by analyzing a case study where the mono-crystalline silicon extraction and refinement processes occur in China, while the final assembly of the panel is in The Netherlands, generating 118 kg of CO2 equivalents per square meter of solar panel. The results suggest that it is generally beneficial to operate solar electric vehicles in countries with a high irradiation index. However, when the local electricity mix already displays a low carbon intensity, the additional emissions introduced by the panel are unnecessary, requiring a longer vehicle lifetime to reach an advantageous emission balance.

eess.SY↗

arXiv2024

Geometric Scaling Laws for Axial Flux Permanent Magnet Motors in In-Wheel Powertrain Topologies

In this paper, we present geometric scaling models for axial flux motors (AFMs) to be used for in-wheel powertrain design optimization purposes. We first present a vehicle and powertrain model, with emphasis on the electric motor model. We construct the latter by formulating the analytical scaling laws for AFMs, based on the scaling concept of RFMs from the literature, specifically deriving the model of the main loss component in electric motors: the copper losses. We further present separate scaling models of motor parameters, losses and thermal models, as well as the torque limits and cost, as a function of the design variables. Second, we validate these scaling laws with several experiments leveraging high-fidelity finite-element simulations. Finally, we define an optimization problem that minimizes the energy consumption over a drive cycle, optimizing the motor size and transmission ratio for a wide range of electric vehicle powertrain topologies. In our study, we observe that the all-wheel drive topology equipped with in-wheel AFMs is the most efficient, but also generates the highest material cost.

eess.SY↗

arXiv2024

Double-tough and ultra-strong ceramics: leveraging multiscale toughening mechanisms through Bayesian Optimization

We present an optimization-driven approach to creating a double-tough ceramic material with a brick-and-mortar microstructure, where the mortar is itself transformation-toughened, engineered with the goal of simultaneously achieving high strength and fracture toughness levels. Specifically, we design a material where high-strength alumina bricks are interconnected via a ceria-stabilized zirconia mortar. As the design of such a material, driven by multiscale toughening mechanisms, requires a laborious trial-and-error approach, we propose a Bayesian optimization framework as an integral part of our methodology to streamline and accelerate the design process. We use a Gaussian process to emulate the material's mechanical response and implement a cost-aware batch Bayesian optimization to efficiently identify optimal design process parameters, accounting for the cost of experimentally varying them. This approach expedites the optimization of the material's mechanical properties. As a result, we develop a bio-inspired all-ceramic composite that exhibits an exceptional balance between bending strength (704 MPa), and fracture toughness (13.6 MPa m^0.5).

physics.app-ph↗

arXiv2024

On Accessibility Fairness in Intermodal Autonomous Mobility-on-Demand Systems

Research on the operation of mobility systems so far has mostly focused on minimizing cost-centered metrics such as average travel time, distance driven, and operational costs. Whilst capturing economic indicators, such metrics do not account for transportation justice aspects. In this paper, we present an optimization model to plan the operation of Intermodal Autonomous Mobility-on-Demand (I-AMoD) systems, where self-driving vehicles provide on-demand mobility jointly with public transit and active modes, with the goal to minimize the accessibility unfairness experienced by the population. Specifically, we first leverage a previously developed network flow model to compute the I-AMoD system operation in a minimum-time manner. Second, we formally define accessibility unfairness, and use it to frame the minimum-accessibility-unfairness problem and cast it as a linear program. We showcase our framework for a real-world case-study in the city of Eindhoven, NL. Our results show that it is possible to reach an operation that is on average fully fair at the cost of a slight travel time increase compared to a minimum-travel-time solution. Thereby we observe that the accessibility fairness of individual paths is, on average, worse than the average values obtained from flows, setting the stage for a discussion on the definition of accessibility fairness itself.

eess.SY↗

arXiv2024

Multi-objective Optimal Trade-off Between V2G Activities and Battery Degradation in Electric Mobility-as-a-Service Systems

This paper presents optimization models for electric Mobility-as-a-Service systems, whereby electric vehicles not only provide on-demand mobility, but also perform charging and Vehicle-to-Grid (V2G) operations to enhance the fleet operator profitability. Specifically, we formulate the optimal fleet operation problem as a mixed-integer linear program, with the objective combining of operational costs and revenues generated from servicing requests and grid electricity sales. Our cost function explicitly captures battery price and degradation, reflecting their impact on the fleet total cost of ownership due to additional charging and discharging activities. Simulation results for Eindhoven, The Netherlands, show that integrating V2G activities does not compromise the number of travel requests being served. Moreover, we emphasize the significance of accounting for battery degradation, as the costs associated with it can potentially outweigh the revenues stemming from V2G operations.

eess.SY↗

arXiv2024

Ride-pooling Electric Autonomous Mobility-on-Demand: Joint Optimization of Operations and Fleet and Infrastructure Design

This paper presents a modeling and design optimization framework for an Electric Autonomous Mobility-on-Demand system that allows for ride-pooling, i.e., multiple users can be transported at the same time towards a similar direction to decrease vehicle hours traveled by the fleet at the cost of additional waiting time and delays caused by detours. In particular, we first devise a multi-layer time-invariant network flow model that jointly captures the position and state of charge of the vehicles. Second, we frame the time-optimal operational problem of the fleet, including charging and ride-pooling decisions as a mixed-integer linear program, whereby we jointly optimize the placement of the charging infrastructure. Finally, we perform a case-study using Manhattan taxi-data. Our results indicate that jointly optimizing the charging infrastructure placement allows to decrease overall energy consumption of the fleet and vehicle hours traveled by approximately 1% compared to an heuristic placement. Most significantly, ride-pooling can decrease such costs considerably more, and up to 45%. Finally, we investigate the impact of the vehicle choice on the energy consumption of the fleet, comparing a lightweight two-seater with a heavier four-seater, whereby our results show that the former and latter designs are most convenient for low- and high-demand areas, respectively.

eess.SY↗

arXiv2024

Model Predictive Control Strategies for Electric Endurance Race Cars Accounting for Competitors Interactions

This paper presents model predictive control strategies for battery electric endurance race cars accounting for interactions with the competitors. In particular, we devise an optimization framework capturing the impact of the actions of the ego vehicle when interacting with competitors in a probabilistic fashion, jointly accounting for the optimal pit stop decision making, the charge times and the driving style in the course of the race. We showcase our method for a simulated 1h endurance race at the Zandvoort circuit, using real-life data of internal combustion engine race cars from a previous event. Our results show that optimizing both the race strategy as well as the decision making during the race is very important, resulting in a significant 21s advantage over an always overtake approach, whilst revealing the competitiveness of e-race cars w.r.t. conventional ones.

eess.SY↗

arXiv2024

Fair Artificial Currency Incentives in Repeated Weighted Congestion Games: Equity vs. Equality

When users access shared resources in a selfish manner, the resulting societal cost and perceived users' cost is often higher than what would result from a centrally coordinated optimal allocation. While several contributions in mechanism design manage to steer the aggregate users choices to the desired optimum by using monetary tolls, such approaches bear the inherent drawback of discriminating against users with a lower income. More recently, incentive schemes based on artificial currencies have been studied with the goal of achieving a system-optimal resource allocation that is also fair. In this resource-sharing context, this paper focuses on repeated weighted congestion game with two resources, where users contribute to the congestion to different extents that are captured by individual weights. First, we address the broad concept of fairness by providing a rigorous mathematical characterization of the distinct societal metrics of equity and equality, i.e., the concepts of providing equal outcomes and equal opportunities, respectively. Second, we devise weight-dependent and time-invariant optimal pricing policies to maximize equity and equality, and prove convergence of the aggregate user choices to the system-optimum. In our framework it is always possible to achieve system-optimal allocations with perfect equity, while the maximum equality that can be reached may not be perfect, which is also shown via numerical simulations.

cs.GT↗

arXiv2023

Optimal Endurance Race Strategies for a Fully Electric Race Car under Thermal Constraints

This paper presents a bi-level optimization framework to compute the maximum-distance race strategies for a fully electric endurance race car, whilst accounting for the low-level vehicle dynamics and the thermal limitations of the powertrain components. Thereby, the lower level computes the minimum-stint-time for a given charge time 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 low-level vehicle dynamics and thermal 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 in a few seconds to the global optimum with off-the-shelf optimization algorithms. Finally, we showcase our framework for a simulated 6h race around the Zandvoort circuit. Our results show that the optimal race strategy can involve partially charging the battery, and that, compared to the case where the stints are optimized for a fixed number of pit stops, jointly optimizing the stints and number of pit stops can significantly increase the driven distance and hence race performance by several laps.

math.OC↗

arXiv2023

A Time-invariant Network Flow Model for Ride-pooling in Mobility-on-Demand Systems

This paper presents a framework to incorporate ride-pooling from a mesoscopic point of view, within time-invariant network flow models of Mobility-on-Demand systems. The resulting problem structure remains identical to a standard network flow model, a linear problem, which can be solved in polynomial time for a given ride-pooling request assignment. In order to compute such a ride-pooling assignment, we devise a polynomial-time knapsack-like algorithm that is optimal w.r.t. the minimum user travel time instance of the original problem. Finally, we conduct two case studies of Sioux Falls and Manhattan, where we validate our models against state-of-the-art time-varying results, and we quantitatively highlight the effects that maximum waiting time and maximum delay thresholds have on the vehicle hours traveled, overall pooled rides and actual delay experienced. We show that for a sufficient number of requests, with a maximum waiting time and delay of 5 minutes, it is possible to ride-pool more than 80% of the requests for both case studies. Last, allowing for four people ride-pooling can significantly boost the performance of the system.

eess.SY↗

arXiv2023

Time-optimal Design and Control of Electric Race Cars Equipped with Multi-speed Transmissions

This paper presents a framework to jointly optimize the design and control of an electric race car equipped with a multiple-gear transmission, specifically accounting for the discrete gearshift dynamics. We formulate the problem as a mixed-integer optimal control problem, and deal with its complexity by combining convex optimization and Pontryagin's Minimum Principle in a computationally efficient iterative algorithm satisfying necessary conditions for optimality upon convergence. Finally, we leverage our framework to compute the achievable lap time of a race car equipped with a fixed-gear transmission, a continuously variable transmission and a multiple-gear transmission with 2 to 4 speeds, revealing that a multiple-gear transmission can strike the best trade-off in terms of electric motor control, and transmission weight and efficiency, ultimately yielding the overall best lap time.

eess.SY↗