Robust Modeling for Optimal Control of Parallel Hybrids With Dynamic Programming

The aim of this work is to provide insight and guidelines for engineers and researchers when developing hybrid powertrain models to be employed in a dynamic programming optimal control algorithm. In particular, we focus on the advantages and disadvantages of the various control sets that can be used to characterize the power flow (e.g. the engine torque or a torque-split coefficient). Dynamic programming is the reference optimal control technique for hybrid electric vehicles. However, its practical implementation is not exempt from numerical issues which may hamper its accuracy. Amongst these, some are directly related to the different modeling choices that can be made when defining the system dynamics of the powertrain. To treat these issues, we first define four relevant evaluation criteria: control bounds definition, numerical efficiency, model complexity and interpretability. Then, we introduce eight different control sets and we discuss and compare them in light of these criteria. This discussion is supported by an extensive set of numerical experiments on a p2 parallel hybrid. Finally, we revisit our analysis and simulation results to draw modeling recommendations

DynaProg: Deterministic Dynamic Programming solver for finite horizon multi-stage decision problems

DynaProg is an open-source MATLAB toolbox for solving multi-stage deterministic optimal decision problems using Dynamic Programming. This class of optimal control problems can be solved with Dynamic Programming (DP), which is a well-established optimal control technique suited for highly non-linear dynamic systems. Unfortunately, the numerical implementation of Dynamic Programming can be challenging and time consuming, which may discourage researchers from adopting it. The toolbox addresses these issues by providing a numerically fast DP optimization engine wrapped in a simple interface that allows the user to set up an optimal control problem in a straightforward yet flexible environment, with no restrictions on the controlled system’s simulation model. Therefore, it enables researchers to easily explore the usage of Dynamic Programming in their fields of expertise. Thorough documentation and a set of step-by-step examples complete the toolbox, thus allowing for easy deployment and providing insight of the optimization engine. Finally, the source code’s classoriented design allows researchers experienced in Dynamic Programming to extend the toolbox if needed

Impact of Predictive Battery Thermal Management for a 48V Hybrid Electric Vehicle

Overheating of battery packs in electrified vehicles is detrimental to their lifetime and performance. Unfortunately, designing a control strategy that ensures battery protection without jeopardizing fuel economy is not a straightforward task. In this paper, we investigate battery temperature-sensitive optimal energy management for a 48V mild-hybrid electric vehicle to prevent overheating with minimal fuel consumption increase. Indeed, this family of hybrid architectures is challenging due to the absence of an active cooling system.In particular, we modeled a p0 parallel-hybrid with a 48V battery pack and we employed dynamic programming to numerically investigate the fuel economy capability while tracking the battery pack temperature.First, we tuned a battery current-constrained powertrain control strategy in order to avoid battery overheating, which could be easily implemented on-board. Then, we implemented a predictive temperature-constrained strategy that exploits the a priori knowledge of driving conditions and temperature constraints to maximize fuel economy.Results show that both strategies are able to meet the battery temperature constraints, although the predictive temperature-constrained control strategy outperforms the current-constrained strategy in terms of fuel economy. This case study demonstrates the theoretical benefits of a predictive battery thermal management for 48V mild hybrids

Cooperative Adaptive Cruise Control Based on Reinforcement Learning for Heavy-Duty BEVs

Advanced driver assistance systems (ADAS) are playing an increasingly important role in supporting the driver to create safer and more efficient driving conditions. Among all ADAS, adaptive cruise control (ACC) is a system that provides consistent aid, especially in highway mobility, guaranteeing safety by minimizing the possible risk of collision due to variations in the speed of the vehicle in front, automatically adjusting the vehicle velocity and maintaining the correct spacing. Theoretically, this type of system also makes it possible to optimize road throughput, increasing its capacity and reducing traffic congestion. However, it was found in practice that the current generation of ACC systems does not guarantee the so-called string stability of a vehicle platoon and can therefore lead to an actual decrease in traffic capacity. To overcome these issues, new cooperative adaptive cruise control (CACC) systems are being proposed that exploit vehicle-to-vehicle (V2V) connectivity, which can provide additional safety and robustness guarantees and introduce the possibility of concretely improving traffic flow stability

End-of-Life Impact on the Cradle-to-Grave LCA of Light-Duty Commercial Vehicles in Europe

A cradle-to-grave life cycle assessment focused on end-of-life (EoL) was conducted in this study for three configurations of a light-duty commercial vehicle (LDCV): diesel, compressed natural gas (CNG), and battery electric vehicle (BEV). The aim is to investigate the impact of recycling under two EoL scenarios with different allocation methods. The first is based on the traditional avoided burden method, while the second is based on the circular footprint formula (CFF) developed by the European Commission. For each configuration, a detailed multilevel waste management scheme was developed in compliance with the 2000/53/CE directive and ISO22628 standard. The results showed that the global warming potential (GWP) impact under the CFF method is significantly greater when compared to the avoided burden method because of the A-parameter, which allocates the burdens and benefits between the two connected product systems. Furthermore, in all configurations and scenarios, the benefits due to the avoided production of virgin materials compensate for the recycling burdens within GWP impact. The main drivers of GWP reduction are steel recycling for all of the considered LDCVs, platinum, palladium, and rhodium recycling for the diesel and CNG configurations, and Li-ion battery recycling for the BEV configuration. Finally, the EoL stage significantly reduces the environmental impact of those categories other than GWP

Battery temperature aware equivalent consumption minimization strategy for mild hybrid electric vehicle powertrains

An energy management strategy for mild hybrids that prevents battery overheating is introduced in this digest. Energy management strategy design for mild hybrids requires particular care to prevent overheating of the battery pack as they typically do not have an active cooling system. To tackle this issue, we extend the well-known equivalent consumption minimization strategy approach to develop a real-time capable fuel-optimal controller that is sensitive to the battery’s thermal dynamics and that can enforce constraints on its temperature. The rationale for our formulation is developed using Pontryagin’s minimum principle from optimal control theory. The same principle is also used to design an off-line numerical procedure for the energy management strategy’s calibration. The effectiveness of the procedure is corroborated by numerical experiments on two different drive cycles, whose results are also compared with the solution obtained with a dynamic programming algorithm. Several peculiar aspects of our solution procedure, such as the method used to incorporate state constraints and the approximate boundary value problem solution method using a particle swarm optimization algorithm, are also detailed and discussed. The proposed controller is computationally light-weight and can be readily extended to on-line control provided that a suitable co-state selection procedure is employed, based on the data collected by using our calibration method on a large number of driving missions

El derecho a la salud mental: viejos problemas, nuevos desafíos

Fil: Arnao, Magdalena. Universidad Nacional de Córdoba. Facultad de Filosofía y Humanidades. Escuela de Filosofía; Argentina.Fil: Arnao, Magdalena. Universidad Nacional de Córdoba. Facultad de Psicología; Argentina.Fil: Arnao, Magdalena. Mesa de Salud Mental y Derechos Humanos. Observatorio de Salud Mental y Derechos Humanos; Argentina.Fil: Szlejcher, Anna. Universidad Nacional de Córdoba. Facultad de Filosofía y Humanidades. Escuela de Archivología; Argentina.Fil: Szlejcher, Anna. Mesa de Salud Mental y Derechos Humanos. Observatorio de Salud Mental y Derechos Humanos; Argentina.Fil: Bocco, Graciela. Universidad Nacional de Córdoba. Facultad de Filosofía y Humanidades; Argentina.Fil: Bocco, Graciela. Universidad Nacional de Córdoba. Facultad de Psicología; Argentina.Fil: Bocco, Graciela. Mesa de Salud Mental y Derechos Humanos. Observatorio de Salud Mental y Derechos Humanos; Argentina.Fil: Caminada, María Paz. Universidad Nacional de Córdoba. Facultad de Psicología; Argentina.Fil: Caminada, María Paz. Mesa de Salud Mental y Derechos Humanos. Observatorio de Salud Mental y Derechos Humanos; Argentina.Fil: Buhlman, Soledad. Universidad Nacional de Córdoba. Facultad de Psicología; Argentina.Fil: Buhlman, Soledad. Mesa de Salud Mental y Derechos Humanos. Observatorio de Salud Mental y Derechos Humanos; Argentina.Fil: Burijovich, Jacinta. Universidad Nacional de Córdoba. Facultad de Psicología; Argentina.Fil: Burijovich, Jacinta. Mesa de Salud Mental y Derechos Humanos. Observatorio de Salud Mental y Derechos Humanos; Argentina.Fil: Carpio, Sol de. Universidad Nacional de Córdoba. Facultad de Psicología; Argentina.Fil: Carpio, Sol del. Mesa de Salud Mental y Derechos Humanos. Observatorio de Salud Mental y Derechos Humanos; Argentina.Fil: Chena, Marina. Universidad Nacional de Córdoba. Facultad de Psicología; Argentina.Fil: Chena, Marina. Mesa de Salud Mental y Derechos Humanos. Observatorio de Salud Mental y Derechos Humanos; Argentina.Fil: Ase, Iván. Universidad Nacional de Córdoba. Facultad de Psicología; Argentina.Fil: Ase, Iván. Mesa de Salud Mental y Derechos Humanos. Observatorio de Salud Mental y Derechos Humanos; Argentina.Fil: Yoma, Solana. Universidad Nacional de Córdoba. Facultad de Psicología; Argentina.Fil: Yoma, Solana. Mesa de Salud Mental y Derechos Humanos. Observatorio de Salud Mental y Derechos Humanos; Argentina.Fil: Blanes, Paola. Universidad Nacional de Córdoba. Facultad de Psicología; Argentina.Fil: Blanes. Paola. Mesa de Salud Mental y Derechos Humanos. Observatorio de Salud Mental y Derechos Humanos; Argentina.Fil: Barrault, Omar. Universidad Nacional de Córdoba. Facultad de Psicología; Argentina.Fil: Barrault, Omar. Mesa de Salud Mental y Derechos Humanos. Observatorio de Salud Mental y Derechos Humanos; Argentina.Fil: Herranz, Melisa. Universidad Nacional de Córdoba. Facultad de Psicología; Argentina.Fil: Herranz, Melisa. Mesa de Salud Mental y Derechos Humanos. Observatorio de Salud Mental y Derechos Humanos; Argentina.Fil: Berra, Cecilia. Universidad Nacional de Córdoba. Facultad de Psicología; Argentina.Fil: Berra, Cecilia. Mesa de Salud Mental y Derechos Humanos. Observatorio de Salud Mental y Derechos Humanos; Argentina.Fil: Miretti, Jeremías. Universidad Nacional de Córdoba. Facultad de Psicología; Argentina.Fil: Miretti, Jeremías. Mesa de Salud Mental y Derechos Humanos. Observatorio de Salud Mental y Derechos Humanos; Argentina.Fil: Díaz, Rodrigo. Universidad Nacional de Córdoba. Facultad de Psicología; Argentina.Fil: Díaz, Rodrigo. Mesa de Salud Mental y Derechos Humanos. Observatorio de Salud Mental y Derechos Humanos; Argentina.Fil: Costa, Maricel. Universidad Nacional de Córdoba. Facultad de Psicología; Argentina.Fil: Costa, Maricel. Mesa de Salud Mental y Derechos Humanos. Observatorio de Salud Mental y Derechos Humanos; Argentina.Fil: Lesta, Liz. Universidad Nacional de Córdoba. Facultad de Psicología; Argentina.Fil: Lesta, Liz. Mesa de Salud Mental y Derechos Humanos. Observatorio de Salud Mental y Derechos Humanos; Argentina.Fil: Castagno, Mariel. Universidad Nacional de Córdoba. Facultad de Psicología; Argentina.Fil: Castagno, Mariel. Mesa de Salud Mental y Derechos Humanos. Observatorio de Salud Mental y Derechos Humanos; Argentina.Fil: Correa, Ana. Universidad Nacional de Córdoba. Facultad de Psicología; Argentina.Fil: Correa, Ana. Mesa de Salud Mental y Derechos Humanos. Observatorio de Salud Mental y Derechos Humanos; Argentina.Fil: Atala, Laura. Universidad Nacional de Córdoba. Facultad de Psicología; Argentina.Fil: Atala, Laura. Mesa de Salud Mental y Derechos Humanos. Observatorio de Salud Mental y Derechos Humanos; Argentina.Fil: Carrizo, Cecilia. Universidad Nacional de Córdoba. Facultad de Psicología; Argentina.Fil: Carrizo, Cecilia. Mesa de Salud Mental y Derechos Humanos. Observatorio de Salud Mental y Derechos Humanos; Argentina.Fil: Gómez, Agustina. Universidad Nacional de Córdoba. Facultad de Psicología; Argentina.Fil: Gómez, Agustina. Mesa de Salud Mental y Derechos Humanos. Observatorio de Salud Mental y Derechos Humanos; Argentina.Fil: Merlo, Virginia. Universidad Nacional de Córdoba. Facultad de Psicología; Argentina.Fil: Merlo, Virginia. Mesa de Salud Mental y Derechos Humanos. Observatorio de Salud Mental y Derechos Humanos; Argentina.Fil: Moreno, Liliana. Universidad Nacional de Córdoba. Facultad de Psicología; Argentina.Fil: Moreno, Liliana. Mesa de Salud Mental y Derechos Humanos. Observatorio de Salud Mental y Derechos Humanos; Argentina.Fil: Fonseca, Federico. Universidad Nacional de Córdoba. Facultad de Psicología; Argentina.Fil: Fonseca, Federico. Mesa de Salud Mental y Derechos Humanos. Observatorio de Salud Mental y Derechos Humanos; Argentina.Fil: Illanes, Mariana. Universidad Nacional de Córdoba. Facultad de Psicología; Argentina.Fil: Illanes, Mariana. Mesa de Salud Mental y Derechos Humanos. Observatorio de Salud Mental y Derechos Humanos; Argentina.Fil: Scorza, Diana. Universidad Nacional de Córdoba. Facultad de Psicología; Argentina.Fil: Scorza, Diana. Mesa de Salud Mental y Derechos Humanos. Observatorio de Salud Mental y Derechos Humanos; Argentina.Se refieren las continuidades en torno a la vulneración de los derechos de las personas confinadas en los hospitales psiquiátricos y se profundiza sobre otros problemas existentes en el campo de la salud mentalhttp://www.unc.edu.ar/extension/vinculacion/observatorio-ddhh/informe-mirar-tras-los-muros-2014-1/informe-mirar-tras-los-muros-2014Fil: Arnao, Magdalena. Universidad Nacional de Córdoba. Facultad de Filosofía y Humanidades. Escuela de Filosofía; Argentina.Fil: Arnao, Magdalena. Universidad Nacional de Córdoba. Facultad de Psicología; Argentina.Fil: Arnao, Magdalena. Mesa de Salud Mental y Derechos Humanos. Observatorio de Salud Mental y Derechos Humanos; Argentina.Fil: Szlejcher, Anna. Universidad Nacional de Córdoba. Facultad de Filosofía y Humanidades. Escuela de Archivología; Argentina.Fil: Szlejcher, Anna. Mesa de Salud Mental y Derechos Humanos. Observatorio de Salud Mental y Derechos Humanos; Argentina.Fil: Bocco, Graciela. Universidad Nacional de Córdoba. Facultad de Filosofía y Humanidades; Argentina.Fil: Bocco, Graciela. Universidad Nacional de Córdoba. Facultad de Psicología; Argentina.Fil: Bocco, Graciela. Mesa de Salud Mental y Derechos Humanos. Observatorio de Salud Mental y Derechos Humanos; Argentina.Fil: Caminada, María Paz. Universidad Nacional de Córdoba. Facultad de Psicología; Argentina.Fil: Caminada, María Paz. Mesa de Salud Mental y Derechos Humanos. Observatorio de Salud Mental y Derechos Humanos; Argentina.Fil: Buhlman, Soledad. Universidad Nacional de Córdoba. Facultad de Psicología; Argentina.Fil: Buhlman, Soledad. Mesa de Salud Mental y Derechos Humanos. Observatorio de Salud Mental y Derechos Humanos; Argentina.Fil: Burijovich, Jacinta. Universidad Nacional de Córdoba. Facultad de Psicología; Argentina.Fil: Burijovich, Jacinta. Mesa de Salud Mental y Derechos Humanos. Observatorio de Salud Mental y Derechos Humanos; Argentina.Fil: Carpio, Sol de. Universidad Nacional de Córdoba. Facultad de Psicología; Argentina.Fil: Carpio, Sol del. Mesa de Salud Mental y Derechos Humanos. Observatorio de Salud Mental y Derechos Humanos; Argentina.Fil: Chena, Marina. Universidad Nacional de Córdoba. Facultad de Psicología; Argentina.Fil: Chena, Marina. Mesa de Salud Mental y Derechos Humanos. Observatorio de Salud Mental y Derechos Humanos; Argentina.Fil: Ase, Iván. Universidad Nacional de Córdoba. Facultad de Psicología; Argentina.Fil: Ase, Iván. Mesa de Salud Mental y Derechos Humanos. Observatorio de Salud Mental y Derechos Humanos; Argentina.Fil: Yoma, Solana. Universidad Nacional de Córdoba. Facultad de Psicología; Argentina.Fil: Yoma, Solana. Mesa de Salud Mental y Derechos Humanos. Observatorio de Salud Mental y Derechos Humanos; Argentina.Fil: Blanes, Paola. Universidad Nacional de Córdoba. Facultad de Psicología; Argentina.Fil: Blanes. Paola. Mesa de Salud Mental y Derechos Humanos. Observatorio de Salud Mental y Derechos Humanos; Argentina.Fil: Barrault, Omar. Universidad Nacional de Córdoba. Facultad de Psicología; Argentina.Fil: Barrault, Omar. Mesa de Salud Mental y Derechos Humanos. Observatorio de Salud Mental y Derechos Humanos; Argentina.Fil: Herranz, Melisa. Universidad Nacional de Córdoba. Facultad de Psicología; Argentina.Fil: Herranz, Melisa. Mesa de Salud Mental y Derechos Humanos. Observatorio de Salud Mental y Derechos Humanos; Argentina.Fil: Berra, Cecilia. Universidad Nacional de Córdoba. Facultad de Psicología; Argentina.Fil: Berra, Cecilia. Mesa de Salud Mental y Derechos Humanos. Observatorio de Salud Mental y Derechos Humanos; Argentina.Fil: Miretti, Jeremías. Universidad Nacional de Córdoba. Facultad de Psicología; Argentina.Fil: Miretti, Jeremías. Mesa de Salud Mental y Derechos Humanos. Observatorio de Salud Mental y Derechos Humanos; Argentina.Fil: Díaz, Rodrigo. Universidad Nacional de Córdoba. Facultad de Psicología; Argentina.Fil: Díaz, Rodrigo. Mesa de Salud Mental y Derechos Humanos. Observatorio de Salud Mental y Derechos Humanos; Argentina.Fil: Costa, Maricel. Universidad Nacional de Córdoba. Facultad de Psicología; Argentina.Fil: Costa, Maricel. Mesa de Salud Mental y Derechos Humanos. Observatorio de Salud Mental y Derechos Humanos; Argentina.Fil: Lesta, Liz. Universidad Nacional de Córdoba. Facultad de Psicología; Argentina.Fil: Lesta, Liz. Mesa de Salud Mental y Derechos Humanos. Observatorio de Salud Mental y Derechos Humanos; Argentina.Fil: Castagno, Mariel. Universidad Nacional de Córdoba. Facultad de Psicología; Argentina.Fil: Castagno, Mariel. Mesa de Salud Mental y Derechos Humanos. Observatorio de Salud Mental y Derechos Humanos; Argentina.Fil: Correa, Ana. Universidad Nacional de Córdoba. Facultad de Psicología; Argentina.Fil: Correa, Ana. Mesa de Salud Mental y Derechos Humanos. Observatorio de Salud Mental y Derechos Humanos; Argentina.Fil: Atala, Laura. Universidad Nacional de Córdoba. Facultad de Psicología; Argentina.Fil: Atala, Laura. Mesa de Salud Mental y Derechos Humanos. Observatorio de Salud Mental y Derechos Humanos; Argentina.Fil: Carrizo, Cecilia. Universidad Nacional de Córdoba. Facultad de Psicología; Argentina.Fil: Carrizo, Cecilia. Mesa de Salud Mental y Derechos Humanos. Observatorio de Salud Mental y Derechos Humanos; Argentina.Fil: Gómez, Agustina. Universidad Nacional de Córdoba. Facultad de Psicología; Argentina.Fil: Gómez, Agustina. Mesa de Salud Mental y Derechos Humanos. Observatorio de Salud Mental y Derechos Humanos; Argentina.Fil: Merlo, Virginia. Universidad Nacional de Córdoba. Facultad de Psicología; Argentina.Fil: Merlo, Virginia. Mesa de Salud Mental y Derechos Humanos. Observatorio de Salud Mental y Derechos Humanos; Argentina.Fil: Moreno, Liliana. Universidad Nacional de Córdoba. Facultad de Psicología; Argentina.Fil: Moreno, Liliana. Mesa de Salud Mental y Derechos Humanos. Observatorio de Salud Mental y Derechos Humanos; Argentina.Fil: Fonseca, Federico. Universidad Nacional de Córdoba. Facultad de Psicología; Argentina.Fil: Fonseca, Federico. Mesa de Salud Mental y Derechos Humanos. Observatorio de Salud Mental y Derechos Humanos; Argentina.Fil: Illanes, Mariana. Universidad Nacional de Córdoba. Facultad de Psicología; Argentina.Fil: Illanes, Mariana. Mesa de Salud Mental y Derechos Humanos. Observatorio de Salud Mental y Derechos Humanos; Argentina.Fil: Scorza, Diana. Universidad Nacional de Córdoba. Facultad de Psicología; Argentina.Fil: Scorza, Diana. Mesa de Salud Mental y Derechos Humanos. Observatorio de Salud Mental y Derechos Humanos; Argentina.Ciencia Polític

MPC-Based Cooperative Longitudinal Control for Vehicle Strings in a Realistic Driving Environment

This paper deals with the energy efficiency of cooperative cruise control technologies when considering vehicle strings in a realistic driving environment. In particular, we design a cooperative longitudinal controller using a state-of-the-art model predictive control (MPC) implementation. Rather than testing our controller on a limited set of short maneuvers, we thoroughly assess its performance on a number of regulatory drive cycles and on a set of driving missions of similar length that were constructed based on real driving data. This allows us to focus our assessment on the energetic aspects in addition to testing the controller’s robustness. The analyzed controller, based on linear MPC, uses vehicle sensor data and information transmitted by the vehicle driving the string to adjust the longitudinal trajectory of the host vehicle to maintain a reduced inter-vehicular distance while simul- taneously optimizing energy efficiency. To keep our controller as close as possible to a real-life deployable technology, we also consider passenger comfort in our MPC design, which is a relevant aspect that is often a conflicting objective with respect to energy efficiency. Our simulation scenario is characterized by a homogeneous string of three battery electric vehicles and was modelled in a MATLAB/Simulink environment. An extensive set of simulation experiments forms the basis for our discussion on the energy-saving potential of cooperative driving automation systems

Battery Electric Vehicle Control Strategy for String Stability based on Deep Reinforcement Learning in V2V Driving

This works presents a Reinforcement Learning (RL) agent to implement a Cooperative Adaptive Cruise Control (CACC) system that simultaneously enhances energy efficiency and comfort, while also ensuring string stability. CACC systems are a new generation of ACC which systems rely on the communication of the so-called egovehicle with other vehicles and infrastructure using V2V and/ or V2X connectivity. This enables the availability of robust information about the environment thanks to the exchange of information, rather than their estimation or enabling some redundancy of data. CACC systems have the potential to overcome one typical issue that arises with regular ACC, that is the lack of string stability. String stability is the ability of the ACC of a vehicle to avoid unnecessary fluctuations in speed that can cause traffic jams, dampening these oscillations along the vehicle string rather than amplifying them. In this work, a real-time ACC for a Battery Electric Vehicle, based on a Deep Reinforcement Learning algorithm called Deep Deterministic Policy Gradient (DDPG), has been developed, aiming at maximizing energy savings, and improving comfort, thanks to the exchange of information on distance, speed and acceleration through the exploitation of vehicle-to-vehicle technology (V2V). The aforementioned DDPG algorithm is also designed in order to achieve the string stability. It relies on a multi-objective reward function that is adaptive to different driving cycles. The simulation results show how the agent can obtain energy savings up to 11% comparing the first following vehicle and the Lead on standard cycles and good adaptability to driving cycles different from the training one

Hybridizing Waterborne Transport: Modeling and Simulation of Low-Emissions Hybrid Waterbuses for the City of Venice

Hybrid-electric powertrains are among the most promising technologies for abating emissions from marine vessels in sensitive areas. However, their effectiveness strongly depends on the context they operate into. This paper attempts to evaluate the potential impact on air quality of hybridizing the diesel-powered waterbuses that currently operate in the city of Venice as part of the local public transportation network. Simulation models for conventional, series hybrid and parallel hybrid marine powertrains were developed and applied to the typical operational mission of one of these waterbuses. For the hybrid powertrains, an Energy Management Strategy is also obtained using a Dynamic Programming - based optimization algorithm. The results show that both hybrid architectures have high emission-reducing potential, with the series hybrid offering the greatest benefits