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Knowledge and monitoring of invasive species are fundamental measures to determine the short- and long-term effect on invaded ecosystems, in addition to developing strategies to control the problem or its specific solution. In this context, the lionfish is an invasive species that worries managers and scientists of fisheries and marine conservation, this is due to the affected area that spread starting from the east coast of the United States to the coasts of Brazil and it is recently extending to include the Mediterranean Sea. The diet of the invasive fish is small species of fish, crustaceans and invertebrates; the consequent damage is the decrease of food for species at the next level of the food chain and the lack of species to keep coral reefs healthy. In this paper, we propose a lionfish detection system that will be installed in an autonomous underwater vehicle, as part of a monitoring strategy that will allow real-time determination of the number of Lionfish, their location and without human intervention. We compared two detection systems, namely YOLOv4 and SSD-Mobilenet-v2, by training with cross-validation and evaluation with the test set we obtained the best model with 63.66% recall, 89.79% precision, and 79.15% mAP with images in the natural environment, implemented on NVIDIA's Jetson Nano embedded system.

Products appearances are made of design choices influencing the way products are perceived. Products semantics is a methods used to understand and anticipate this phenomena. Nowadays, consumers consider sport products not only as “sport” but also as “health” products. Designers may then develop them as “sport-health” products. However, perception of “sport-health” products may vary according to the need to fit sport or health context of use. We present in this paper our experimental approach to understand the influence of sport and health contexts on “sport-health” semantics.

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Nowadays, the high competitiveness of a company depends on its ability to deal quickly and cost-effectively with the market disruptions. For this reason, companies should have an agile and flexible supply chains to solve problems related to the market fluctuation. In fact, supply chain reconfigurability is the ability to modify their capacity and functionality at the lowest cost. The objective of this article is to assess reconfigurability based on its characteristics (modularity, scalability, integrability, convertibility, diagnosability and customization) using Fuzzy logic. For this purpose, a quantitative evaluation of reconfigurability is proposed. In order to validate our assessment model, a case study is applied to evaluate the degree of reconfigurability after supply chain reconfiguration.

Smart environments are currently overpowering traditional Human-Computer Interaction (HCI) approaches for people's everyday life applications. Proxemic interaction is an emerging area for improving HCI experiences in such environments, causing the so-called proxemic environments to emerge. Proxemic interaction describes how the five dimensions (i.e., Distance, Identity, Location, Movement, and Orientation - DILMO) can be used to define interactions among people and digital devices. Current studies in this area are focused on developing proxemic applications with specific functionalities, based on a toolkit that allows developers to obtain DILMO information from sensors. However, there exists a notable lack of general approaches able to support the whole implementation process, starting from the modelling of proxemic environments to represent general proxemics behaviours, and finalizing with the development of specific applications. In order to facilitate the integration of proxemic capabilities in HCI, we propose an approach for modelling proxemic environments based on a graphical Domain-Specific Language (DSL). The DSL allows non-specialist designers to express proxemic interactions for modelling proxemic environments and supports the development process. In order to show its suitability and functionality, we have implemented a prototype to design proxemic behaviours in different proxemic environments.

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Aiming to take full advantage of Electric Vehicles' (EVs) batteries despite unpredictability in their arrival and departure time, in this paper, a two-level hierarchical model predictive controller coupled with an innovative charging-discharging scheduler for EVs in building microgrids is proposed. Besides providing a complete framework for its design, this paper also analyses different scenarios of EVs' batteries exploitation that would be beneficial for both building microgrids and EVs' owners. The simulation results conducted in MATLAB Simulink demonstrated that the proposed hierarchical controller could assure that most EVs are completely charged before their departure time while enabling building microgrids to increase their annual photovoltaic self-consumption rate by about 3 percent point compared to the uncontrolled strategy.

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International audience; Although linear electromagnetic energy harvester (LEH) is a promising technique for converting energy in a vehicle suspension, due to the large displacements, one of the main drawbacks of the solutions inside the vehicle is still their size and complexity. To address this issue, this paper focuses on the design and fabrication of a fully embedded LEH without any modification of the suspension initial structure. After a determination of the electrical, mechanical and electromechanical parameters using a Finite Element analysis, the dynamic efficiency is highlighted with a global Bond Graph model. This formalism is well adapted to simulate energy transfers inside multiphysic systems and to reduce the computational time, whereas the finite element model is not exploitable for a complete suspension simulation. In order to validate the Bond Graph simulation results, an embedded prototype has been built and tested in a laboratory environment. The embedded LEH system delivers around 10 W for a solicitation of linear velocity of 1 m/s which is sufficient to power a classical electronic circuit which is in good correlation with the measured ones and significant power has been obtained.

The present work is focused on one Additive Manufacturing (AM) process – Laser powder Metal Deposition (LMD-p) – and on one metallic alloy – Ti-6Al-4V. State of the art on LMD-p on Ti-6Al-4V alloy shows that three kinds of process parameters influence mechanical properties of building parts: raw materials (powder and substrate), machine parameters (Laser Power (P), Powder Flow (F) and Building Speed (V)), and building strategies (part orientation, waiting time between layers, etc.). Thus, this paper relates to first manufacturing investigations on small sized specimens (bead, wall and block) with the aim of providing a better knowledge about the first steps of manufacturing. Particularly, this paper is dedicated to the study of machine parameters (P, F and V). First, the influence of each machine parameter on 28 beads is studied separately. The geometrical aspect (high, width, dilution) of each bead is microscopically measured. Similarly, combinations of parameters (P/F, Energy Density and Powder Density) are introduced to increase parameters degree of freedom. First results show that P, V and F have a major influence on the beads’ geometry. In addition, a window process map is plotted and allows determining functional areas of machine parameters. From this map, walls (vertical superposition of one bead) are manufactured and microscopically observed. Functional sets of parameters from walls are selected and blocks can be built.

International audience; The present work is focused on one Additive Manufacturing (AM) process – Laser powder Metal Deposition (LMD-p) – and on one metallic alloy – Ti-6Al-4V. State of the art on LMD-p on Ti-6Al-4V alloy shows that three kinds of process parameters influence mechanical properties of building parts: raw materials (powder and substrate), machine parameters (Laser Power (P), Powder Flow (F) and Building Speed (V)), and building strategies (part orientation, waiting time between layers, etc.). Thus, this paper relates to first manufacturing investigations on small sized specimens (bead, wall and block) with the aim of providing a better knowledge about the first steps of manufacturing. Particularly, this paper is dedicated to the study of machine parameters (P, F and V).First, the influence of each machine parameter on 28 beads is studied separately. The geometrical aspect (high, width, dilution) of each bead is microscopically measured. Similarly, combinations of parameters (P/F, Energy Density and Powder Density) are introduced to increase parameters degree of freedom. First results show that P, V and F have a major influence on the beads’ geometry. In addition, a window process map is plotted and allows determining functional areas of machine parameters. From this map, walls (vertical superposition of one bead) are manufactured and microscopically observed. Functional sets of parameters from walls are selected and blocks can be built.

A life cycle assessment (LCA) was conducted on an innovative concrete 3D printing system, offering the following main advantages: (1) additive and subtractive capabilities, allowing for the automated post-processing of printed parts, including operations such as surface polishing, grooving and drilling and (2) the use of a cable robot, which is less expensive, lighter, more transportable, more energy-efficient and more easily reconfigurable than alternatives such as gantry-type systems. The production of a 4-m height structural pillar was assessed, comparing it to production with traditional methods, namely, using a mould. The study included the entire supply chain of the 3D printing equipment, operation and end-of-life, based on real data from the design and operation of a demonstration plant installed in Spain. Data for traditional construction was based on literature and expert judgement. The 3D production process included printing the pillar perimeter in four pieces with 3D printing concrete, transporting to the construction site and reinforcing and casting with conventional concrete. Traditional production involved reinforcing and casting with the mould on-site. The results show that when only one pillar needs to be produced, 3D printing has a lower environmental impact in all the environmental indicators assessed when compared to using a mould that is discarded after a single use. As an example, GHG emissions are lower by 38%. It was also found that the contribution of 3D printing to the environmental impact of producing a pillar is almost negligible, representing less than 1% of the pillar’s total GHG emissions. However, when the same pillar needs to be produced in higher numbers, the results show that 3D printing and conventional production have a similar environmental impact, given that the mould used in conventional production can be reused, becoming a comparatively efficient option.

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Social media and other platforms on Internet are commonly used to communicate and generate information. In many cases, this information is not validated, which makes it difficult to use and analyze. Although there exist studies focused on information validation, most of them are limited to specific scenarios. Thus, a more general and flexible architecture is needed, that can be adapted to user/developer requirements and be independent of the social media platform. We propose a framework to automatically and in real-time perform credibility analysis of posts on social media, based on three levels of credibility: Text, User, and Social. The general architecture of our framework is composed of a front-end, a light client proposed as a web plug-in for any browser; a back-end that implements the logic of the credibility model; and a third-party services module. We develop a first version of the proposed system, called T-CREo (Twitter CREdibility analysis framework) and evaluate its performance and scalability. In summary, the main contributions of this work are: the general framework design; a credibility model adaptable to various social networks, integrated into the framework; and T-CREo as a proof of concept that demonstrates the framework applicability and allows evaluating its performance for unstructured information sources; results show that T-CREo qualifies as a highly scalable real-time service. The future work includes the improvement of T-CREo implementation, to provide a robust architecture for the development of third-party applications, as well as the extension of the credibility model for considering bots detection, semantic analysis and multimedia analysis. This research was supported by the FONDO NACIONAL DE DESARROLLO CIENTIFICO, TECNOLOGICO Y DE INNOVACION TECNOLOGICA ~ FONDECYT as an executing entity of CONSEJO NACIONAL DE CIENCIA, TECNOLOGIA E INNOVACION TECNOLOGICA - CONCYTEC under grant agreement No. 01-2019-FONDECYT-BM-INC.INV in the project RUTAS: Robots for Urban Tourism centers, Autonomous and Semantic-based.

In this paper a hierarchical energy management strategy design methodology for total cost of ownership management at fleet level is proposed. The decisions are taken from the fleet level point of view, to optimize the whole fleet based on the hierarchical decision maker and management, composed of three levels. The outer part is the offline route-to-bus data exploitation and decision maker, aiming to establish the dynamic programming optimization design. The next level is the offline optimization bus-to-route. Based on the previous decision, the neuro-fuzzy learns from the global optimal solutions. Finally, the trained fuzzy-logic strategy is used to manage the online operation. This fleet is re-organized and the online operation energy management strategy is updated throughout the bus lifetime. These decisions are made based on the evaluated battery lifetime of the fleet, with the aim to meet the planned total cost of ownership requirements. The total cost of ownership for the bus-to-route energy management strategy, has been improved a 7.65 % at fleet level against a charge-sustaining charge-depleting strategy. In the route-to-bus fleet re-organization and update, up to 5.51 % total cost of ownership improvement at fleet level has been obtained against not applying the fleet management.

Twitter is one of the most popular information source available on the Web. Thus, there exist many studies focused on analyzing the credibility of the shared information. Most proposals use either Twitter API or web scraping to extract the data to perform such analysis. Both extraction techniques have advantages and disadvantages. In this work, we present a study to evaluate their performance and behavior. The motivation for this research comes from the necessity to know ways to extract online information in order to analyze in real-time the credibility of the content posted on the Web. To do so, we develop a framework which offers both alternatives of data extraction and implements a previously proposed credibility model. Our framework is implemented as a Google Chrome extension able to analyze tweets in real-time. Results report that both methods produce identical credibility values, when a robust normalization process is applied to the text (i.e., tweet). Moreover, concerning the time performance, web scraping is faster than Twitter API, and it is more flexible in terms of obtaining data; however, web scraping is very sensitive to website changes.

Until now, construction is an area where most activities are artisanal. Concrete is linked to the masonry trade where few machines are used. Moreover, this material is mainly used for building construction, but it can be used for other applications like urban furniture for example where a mold is necessary to pour and maintain the concrete until it is dry. This method can be expensive for unique part because of the implementation time. Moreover, the part geometry is limited by the mold shape. For a few years, the additive manufacturing is used with the concrete material, mainly to build house walls but without finishing step to have smooth or assembling surfaces. So, it is composed by the layers of concrete and it is difficult to assembly two parts for example because of the shape but also dimensional accuracy. For these reasons, in the frame of the HINDCON project, a low-cost subtractive task has been introduced, to perform post-printing machining with respect of geometrical constraints.

A comprehensive numerical model is developed for the simulation of the laser-assisted automated tape placement process of carbon fiber/thermoplastic composites. After being heated with a laser, the thermoplastic is welded with the help of a consolidation roller onto a substrate made up of layers of tapes bonded onto one another. Under the pressure applied by the roller, the thermoplastic flows and the tape reaches its final thickness. The numerical model is developed in three sequential steps that can be used to identify the required pressure and temperature distribution to achieve a good bond. Firstly, a heat transfer simulation is performed to determine the temperature distribution into the incoming tape under the consolidation roller. Secondly, a rheological model is developed to examine the polymer flow under the roller and to obtain the pressure field. Finally, the consolidation level between the substrate and the tape is investigated through the degree of intimate contact, which is related to the processing parameters such as the roller velocity, the laser power density and the compaction force.

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The widespread diffusion of smart and mobile devices continuously connected to the Internet has facilitated the users’ contact and interaction with other people, devices, and with their physical surroundings. Proxemic interaction, derived from proxemics theory, focuses on how Human-Computer Interaction (HCI) works with smart devices, using the five proxemic dimensions: Distance, Identity, Location, Movement, and orientation (DILMO). The current tools for developing proxemic applications require fixed devices that make it difficult to build proxemic mobile apps. In this work, we propose a framework to manage all components in a proxemic environment (i.e., interaction objects and DILMO dimensions that govern the HCI). We demonstrate and evaluate the effectiveness and suitability of our framework, through the development of two proxemic mobile applications, as proof-of-concept.

In this paper, a new power division approach is proposed for a Four-Wires Three-Level Neutral Point Clamped (4W-3L-NPC) inverter used as the unique interface for a Hybrid Energy Storage System (HESS) supplied by a renewable energy system in the context of a Microgrid. The objective of this work is to define the limits of the investigated topology and prove its capacity to manage the power division and power exchange capabilities of a HESS. The proposed power division index is expressed using the DC mean power and AC active power, giving a clearer and simpler insight of the limits which are facilitating the implementation in the final controller. To prove its capability, the investigated approach has been tested in the context of a Microgrid with and without Renewable Energy Sources injection on the DC bus.

The aim of this paper is to propose a battery aging conscious energy management strategy. The initial design of an energy management strategy is a significant point, in order to fulfill the efficiency goals in a short term. However, with the aging, the initial conditions vary. The new trend of digitalization allows to monitor the operation, having the possibility to update the developed first strategy in a long term. Therefore a methodology for updating the energy management strategy throughout the bus lifetime is proposed, in order to improve the operation costs and extend the battery lifetime. This update is based on a dynamic programming optimization, tuning the fuzzy logic membership functions and output. Operation costs improvements up to 32.5% are obtained beside a rule-based strategy and a BT lifetime extension of 5.64% is reached. A sensitivity analysis for identifying the critical factors affecting the plug-in hybrid electric bus energy management strategy has been carried out. Auxiliary consumption variation and battery lifetime throughout the bus lifetime have been identified as the most affecting factors.

Conventional Li-ion battery ageing models, such as electrochemical, semi-empirical and empirical models, require a significant amount of time and experimental resources to provide accurate predictions under realistic operating conditions. At the same time, there is significant interest from industry in the introduction of new data collection telemetry technology. This implies the forthcoming availability of a significant amount of real-world battery operation data. In this context, the development of ageing models able to learn from in-field battery operation data is an interesting solution to mitigate the need for exhaustive laboratory testing. In a series of two papers, a data-driven ageing model is developed for Li-ion batteries under the Gaussian Process framework. A special emphasis is placed on illustrating the ability of the Gaussian Process model to learn from new data observations, providing more accurate and confident predictions, and extending the operating window of the model. The first paper of the series focussed on the systematic modelling and experimental verification of cell degradation through calendar ageing. Conversantly, this second paper addresses the same research challenge when the cell is electrically cycled. A specific covariance function is composed, tailored for use in a battery ageing application. Over an extensive dataset involving 124 cells tested during more than three years, different training possibilities are contemplated in order to quantify the minimal number of laboratory tests required for the design of an accurate ageing model. A model trained with only 26 tested cells achieves an overall mean-absolute-error of 1.04% in the capacity curve prediction, after being validated under a broad window of both dynamic and static cycling temperatures, Depth-of-Discharge, middle-SOC, charging and discharging C-rates. This investigation work was financially supported by ELKARTEK (CICe2018 - Desarrollo de actividades de investigacion fundamental estrategica en almacenamiento de energia electroquimica y termica para sistemas de almacenamiento hibridos, KK-2018/00098) and EMAITEK Strategic Programs of the Basque Government. In addition, the research was undertaken as a part of ELEVATE project (EP/M009394/1) funded by the Engineering and Physical Sciences Research Council (EPSRC) and partnership with the WMG High Value Manufacturing (HVM) Catapult. Authors would like to thank the FP7 European project Batteries 2020 consortium (grant agreement No. 608936) for the valuable battery ageing data provided during the project.

International audience; Museums aim at offering personalized visits to encourage visitors to visit more than once. Few approaches consider the specific skills of museum professionals when designing tools for this purpose. We conducted a three-step iterative and user-centered design process with 13 museum professionals from six museums. This analysis led us to a main finding: the most complicated task for museum professionals is to explore their design space, composed of all possible visitor profiles for which to create visits. We propose a visualization for this multidimensional design space and six potential interactions on this representation. In an exploration space, we classify them on two axes: the selection approach and the type of interface (GUI versus TUI). We analyze their benefits and limits and, based on a pilot study, we propose insights and questions for future design.

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From a cosmopolitan localism perspective, the circular economy could be described as a web of smaller circular economies where the core development is situated in local areas, like cities, ...

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Conventional Li-ion battery ageing models, such as electrochemical, semi-empirical and empirical models, require a significant amount of time and experimental resources to provide accurate predictions under realistic operating conditions. At the same time, there is significant interest from industry in the introduction of new data collection telemetry technology. This implies the forthcoming availability of a significant amount of real-world battery operation data. In this context, the development of ageing models able to learn from in-field battery operation data is an interesting solution to mitigate the need for exhaustive laboratory testing. In a series of two papers, a data-driven ageing model is developed for Li-ion batteries under the Gaussian Process framework. A special emphasis is placed on illustrating the ability of the Gaussian Process model to learn from new data observations, providing more accurate and confident predictions, and extending the operating window of the model. This first paper focusses on the systematic modelling and experimental verification of cell degradation through calendar ageing. A specific covariance function is composed, tailored for use in a battery ageing application. Over an extensive dataset involving 32 cells tested during more than three years, different training possibilities are contemplated in order to quantify the minimal number of laboratory tests required for the design of an accurate ageing model. A model trained with only 18 tested cells achieves an overall mean-absolute-error of 0.53% in the capacity curves prediction, after being validated under a broad window of both dynamic and static temperature and SOC storage conditions. This investigation work was financially supported by ELKARTEK (CICe2018 -Desarrollo de actividades de investigacion fundamental estrategica en almacenamiento de energia electroquimica y termica para sistemas de almacenamiento hibridos, KK-2018/00098) and EMAITEK Strategic Programs of the Basque Government. In addition, the research was undertaken as a part of ELEVATE project (EP/M009394/1) funded by the Engineering and Physical Sciences Research Council (EPSRC) and partnership with the WMG High Value Manufacturing (HVM) Catapult. Authors would like to thank the FP7 European project Batteries 2020 consortium (grant agreement No. 608936) for the valuable battery ageing data provided during the course of the project.

International audience; Over the past number of years, competence management has become a key issue for companies. Similarly, to the importance of knowledge management, competence management considers an individual’s performance, based on his or her knowledge. Thus, knowledge management becomes an important aspect for companies to manage resources, through the ability of characterizing key competences and evaluating how they are improved through past experiences and roles, thus selecting project team members according to the respective existing skills. This paper focuses on the coordination of design activities in order to propose a tool dedicated to operation-level project managers, to help prepare their technical teams based on the required skills. The goal is to enhance the effectiveness of the teams, as well as their performance in the short-term, as well as the longterm, while coordinating with the human resources department. This work is based on the results of the Aides et assisTances pour la conception, la conduite et leur coupLage par les connAissanceS – Help and support for design, coordination and their coupling by knowledge (ATLAS, 2008 - 2011). This project studies the coupling between systems design and management. We propose an initial tool to manage skills in a design project.

This paper aims to present a novel control strategy for modular multilevel converters (MMC) based on differential flatness theory, in which instantaneous active and reactive power values are considered as the flat outputs. To this purpose, a mathematical model of the MMC taking into account dynamics of the ac-side current and the dc-side voltage of the converter is derived in a d-q reference frame. Using this model, the flat outputs-based dynamic model of MMC is obtained to reach the initial value of the proposed controller inputs. In order to mitigate the negative effects of the input disturbance, model errors, and system uncertainties on the operating performance of the MMC, the integral-proportional terms of the flat output errors are added to the initial inputs. This can be achieved through defining a control Lyapunov function which can ensure the stability of the MMC under various operating points. Moreover, the small-signal linearization method is applied to the proposed flat output-based model to separately evaluate the variation effects of controller inputs on flat outputs. The proficiency of the proposed method is researched via MATLAB simulation. Simulation results highlight the capability of the proposed controller in both steady-state and transient conditions in maintaining MMC currents and voltages, through managing active and reactive power.

A multi-loop control strategy based on a six-order dynamic model of the modular multilevel converter (MMC) is presented in this paper for the high-voltage direct current (HVDC) applications. For the initial analysis of the operation of MMC, a capability curve based on active and reactive power of the MMC is achieved through a part of the six order dynamic equations. According to the MMC’s control aims, the first loop known as the outer loop is designed based on passivity control theory to force the MMC state variables to follow their reference values. As the second loop with the use of sliding mode control, the central loop should provide appropriate performance for the MMC under variations of the MMC’s parameters. Another main part of the proposed controller is defined for the third inner loop to accomplish the accurate generation of reference values. Also, for a deeper analysis of the MMC’s dc link voltage stability, two phase diagrams of the dc-link voltage are assessed. Matlab/Simulink environment is used to thoroughly validate the ability of the proposed control technique for control of the MMC in HVDC application under both load and MMC’s parameters changes.

This paper aims at enhancing the resilience of a photovoltaic-based microgrid equipped with battery storage, supplying a typical commercial building. When extreme weather conditions such as hurricane, tsunami and similar events occur, leading to islanding of the microgrid from the main power grid, it is not expected that the microgrid would be taken out of service for a long time. At the same time, it is not cost effective to make the electrical system to be absolutely reliable to provide service for the customers. The main contribution of this paper lies in its ability to determine the optimal point between the operational cost and grid resilience. In other words, this work proposes an optimal management system of battery energy storage in a way to enhance the resilience of the proposed microgrid while maintaining its operational cost at a minimum level. The optimization is achieved by solving a linear optimization programming problem while the Conditional Value at Risk (CVaR) is incorporated in the objective function. The CVaR is used to account for the uncertainty in the intermittent PV system generated power and that in the electricity price. Simulation analyses are carried out in MATLAB to evaluate the performance of the proposed method. Results reveal that the commercial building microgrid resilience is improved remarkably at a slight increase in the operational cost.

New applications for robots include tasks that were performed by specialized machines such as polish, milling, placement of fibers, among others. The implementation using robots in these new applications has the inaccuracies problems due to the flexibilities within the robot arm or the tooling when external forces are applied. The industrial robot controller does not take into account the flexibilities and the external forces. This paper is an introduction to the work done to improve the precision in the robotic fiber placement, taking into account the deformation in the compaction roller.

This paper proposes a digital control scheme to control operation of a proton exchange membrane fuel cell module of 1.2 kW and a supercapacitor through a DC/DC hybrid converter. A FC is proposed as a primary source of energy along with a SC as an auxiliary source of energy. A control scheme is proposed for control of the proposed system. An experimental setup of the proposed system is implemented in the laboratory, and several scenarios have been defined for tests to verify that the proposed system achieves an excellent output voltage regulation and SC voltage control, under disturbances from FC voltage, load voltage and other perturbations described in results analysis.

International audience; A MicroGrid (MG) is a distributed power system consisting of a number of heterogeneous components having direct/indirect impacts on each other. In order to provide an appropriate collaboration (from several perspectives) between components, we propose a "Digital Ecosystem Cooperative Framework" called DECF. In this paper, we present the clustering algorithm of DECF designed to build Alliances by gathering all the DE heterogeneous components having similar needs and preferences. Conducted simulations showed that the proposed algorithms yield significant results. \textcopyright 2017 The Author(s).

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The aim of this paper is to propose a battery aging conscious energy management strategy. The initial design of an energy management strategy is a significant point, in order to fulfill the efficiency goals in a short term. However, with the aging, the initial conditions vary. The new trend of digitalization allows to monitor the operation, having the possibility to update the developed first strategy in a long term. Therefore a methodology for updating the energy management strategy throughout the bus lifetime is proposed, in order to improve the operation costs and extend the battery lifetime. This update is based on a dynamic programming optimization, tuning the fuzzy logic membership functions and output. Operation costs improvements up to 47 % are obtained beside a rule-based strategy and a BT lifetime extension of 2.94 % is reached.

The aim of this paper is to propose a methodology for managing the battery lifetime of a whole fleet with the aim to improve the total cost of ownership of hybrid electric buses. This approach has been addressed from two points of view the bus-to-route and route-to-bus approaches. The bus-to-route optimization is focused on the energy management strategy generation of each bus of the fleet. A techno-economic, route energetic evaluation and battery aging analysis of the fleet have been performed. From the outcome of this analysis, the buses have been grouped, according the state of health of each bus. Based on the analysis and classification, the route-to-bus approach is applied. This technique lies on both, a re-evaluation of the energy management system and/or the re-organization of the buses according to the state of health of each bus. Increases of BT lifetimes up to 10.7% are obtained with the proposed approach.

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