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Multi-phase dynamic inductive power transfer (D-IPT) systems are capable of achieving uniform power transmission with low control complexity and high efficiency. To achieve acceptable power capabilities, D-IPT systems have to work in resonance configurations. In contrast to transformers, and due to the low coupling coefficient, the compensation is carried out separately in the primary and secondary sides. Consequently, an effect known as bifurcation or pole splitting is created. This causes extra losses in the semiconductors because zero voltage switching (ZVS) is lost. The main objective of this paper is to derive the bifurcation limits for a three-phase D-IPT system. First, the meander coil configuration is introduced. Because this is a system with multiple variables, five assumptions are made to achieve closed-form equations. Therefore, the 36 inductance system is converted into an ideal three inductance problem. With these assumptions, the equations of the coupling, power, and inductance ratio limits are obtained. Afterward, the repercussion of these assumptions is analyzed using illustrations that depict the input impedance angle for various non-ideal conditions. Finally, a 9-kW prototype is used to validate the calculations, analyzing two different operating points: incomplete ZVS and complete ZVS.

International audience; Electric grid, as we nowadays know it, is undergoing a significant transformation. What we are now witnessing is an undoubted change of trend towards a decentralized and decarbonized electric grid, where the electric generation based on local resources will take on special relevance. In this context, the encouragement of collective selfconsumption becomes one of the key issues when it comes to taking steps forward to this end. One of the aspects that will contribute to this aim is the development of a consumptionforecasting tool. Hence, a load-forecasting model based on NARX Neural Network is proposed in the following paper. The prediction of the next day (24h) load profile of an individual building is carried out aiming an optimal management of the flexible loads so to achieve the maximum self-consumption. To ensure a consistent behavior of the NARX Neural Network model, identification and removing of outliers, together with data normalization and fixing common time interval has been carried out. The first results of the research are promising, being obtained a 17,6% MAPE in NARX and 25,19% with LSTM model, both evaluated during a regular week on winter in adverse conditions .

This paper aims to propose a battery aging conscious energy management strategy. The initial design of an energy management strategy is a significant point to fulfill the efficiency goals in the short term. However, with aging, the initial conditions may vary. The new trend of digitalization allows monitoring the operation, having the possibility to improve the performance of the initially proposed strategy in the long term. Therefore, a methodology for updating the energy management strategy along the bus lifetime is intended to improve the operating costs and extend the battery lifetime. This methodology is based on a dynamic programming optimization, tuning the membership functions in a fuzzy logic control. The simulation results show a reduction of the operation costs up to 47% as long as it stands for battery (BT) lifetime extension of around 2.94%., El objetivo de este trabajo es proponer una estrategia inteligente basada en el envejecimiento de la batería de litio-ion instalada abordo de vehículo como aplicación de gestión energética. El diseño inicial de una estrategia de gestión energética (EMS) es un paso significativo para cumplir los objetivos de eficiencia en la operación a corto plazo. Sin embargo, debido al envejecimiento de la batería las condiciones iniciales de la EMS pueden variar. La nueva tendencia hacia la digitalización permite monitorizar la operación, brindando la posibilidad de mejorar el desempeño de la estrategia inicialmente propuesta en el largo plazo. Por lo tanto, se propone una metodología para actualizar la EMS con el objetivo de mejorar los costos de operación y extender la vida útil de la batería. La metodología se basa en una optimización mediante programación dinámica para parametrizar las funciones de pertenencia de un control difuso. Los resultados de simulación muestran una reducción en los costos de operación entorno al 47 % junto con una extensión de la vida útil de la batería de alrededor de 2.94 %.

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.

In this article, a new fuzzy-based predictive control strategy for intelligent power management of a serial hybrid electric bus with a hybrid storage system that combines batteries and supercapacitors is proposed. The main contributions in this document are a fuzzy definition based predictive control for vehicle power management, along with a multi-objective optimization approach to define the size and operation of the ESS. To obtain the best performance in the genset, a methodology is proposed to optimize the operation point selection based on a speed-torque search in the electric generator and combustion engine maps. Based on simulation results, a reduction of 18.5% of the total cost was compared to a traditional rule-based control.

Conventional Li-ion battery ageing models require a significant amount of time and experimental resources to provide accurate predictions under realistic operating conditions. Furthermore, there is still an uncertainty on the validity of purely laboratory data-based ageing models for the accurate ageing prediction of battery systems deployed in field.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 in-field 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, reduce the development cost of ageing models and at the same time ensure the validity of the model for prediction under real operating conditions.In this paper, a holistic data-driven ageing model developed under the Gaussian Process framework is validated with experimental battery ageing data. Both calendar and cycle ageing are considered, to predict the capacity loss within real EV driving scenarios. The model can learn from the driving data progressively observed, improving continuously its performances and providing more accurate and confident predictions.

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. 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.

The prediction accuracy of Lithium-ion (Li-ion) battery ageing models based on laboratory data is uncertain in the context of online prediction. This is due to the difficulty to reproduce realistic operating profiles in laboratory. The development of self-adaptive ageing models, which are updated using the ageing data obtained in operation, allows enhancing the online prediction accuracy and reducing the required characterisation period in laboratory. At the same time, it offers the possibility to maximise systems' profitability, providing useful information to update the energy management strategy and for predictive maintenance purposes. The present study aims at reviewing, classifying and comparing the different self-adaptive Li-ion battery ageing models proposed in the literature. Firstly, the different characteristics influencing the ability of a model to update itself are identified, and a classification is proposed for self-adaptive Li-ion battery ageing modelling methods. Secondly, specific criteria are defined to assess and compare the accuracy and computational cost of the different models, enabling a selection of the most suitable ones. Finally, relevant conclusions are drawn considering the key features required to achieve effective ageing predictions, and concise recommendations are suggested for future self-adaptive Li-ion battery ageing model development.

Learning based energy management strategies are promising methods, due to the high adaptability and the capability to learn from historical data. Widening the scope to a whole fleet, allows to learn from a more extend data-base and wider range of operating conditions. This paper aims to develop a methodology for improving the overall energetic efficiency of a fleet. In this regard, the main contribution lyes on the development of an intelligent decision maker, with the goal to design improved and adapted energy management strategies for each bus operating on a predefined route. The intelligent decision maker is driven by an adaptive neuro-fuzzy inference system technique that learns from the optimal operation optimized with dynamic programming. The obtained results in the developed EMS have shown similarities with the dynamic programming operation, reaching close fuel consumptions, 0.01% of difference and improvements up to 16% of fuel consumption against the initial EMS.

This paper aims to propose a methodology for managing Li-ion battery life for a whole fleet, focusing on improving the total operating costs for hybrid electric buses. This problem is approached in two different ways: bus-to-route and route-to-bus. Bus-to-route optimization focuses on developing an energy management strategy for each bus in the fleet. A techno-economic, energy and battery aging analysis of the fleet was carried out. As per the outcomes of this analysis, buses were grouped according to the state of health of the battery of each bus. Based on this analysis and classification, the route-to-bus approach was applied. This technique involves a re-evaluation of the energy management system and a re-organization of the buses according to the state of health of the battery of each bus. Increases in battery (BT) life of up to 10.7% were obtained using the proposed approach., El objetivo de este artículo es proponer una metodología para la gestión de la vida útil de las baterías de Litio-ion de una flota completa de autobuses eléctrico-híbridos orientado a mejorar el costo total de operación. Esta propuesta se ha abordado desde dos puntos de vista: autobús-a-ruta y ruta-a-autobús. La optimización autobús-a-ruta se enfoca en la generación de la gestión energética de cada autobús perteneciente a la ruta. Para este efecto, se ha llevado a cabo un análisis tecno-económico, energético y de esperanza de vida de las baterías de la flota de vehículos. Partiendo del resultado de este análisis, los vehículos han sido agrupados de acuerdo al parámetro del estado de salud de la batería de cada autobús. Basándose en dicho análisis y clasificación, se ha realizado la aplicación del enfoque ruta-a-autobús. Esta técnica considera tanto una reevaluación de la gestión energética y/o una reorganización de los autobuses de acuerdo al estado de salud de sus baterías. Se ha conseguido un incremento de hasta 10.7 % de la vida útil de las baterías haciendo uso de los enfoques propuestos.

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.

The chapter focuses on a fleet energy management approach with the aim of reducing operation and maintenance costs. A state-of-the-art is presented for the different proposed fleet management approaches. In order to tackle the digitalization challenge of exploiting the large data volume of a fleet of vehicles, a methodology for improving electrified buses energy efficiency at fleet level is proposed. In addition, an energetic analysis of a fleet based on this methodology has been performed. The analyzed fleet is composed of buses with parallel and series configurations and include energy storage systems based on batteries and ultracapacitors. In the first stage, a dynamic programming approach has been applied to determine the initial optimal operation performance for each bus route. Then, several disruptions (e.g., traffic jams, auxiliary consumption, and passenger variations) have been added to the routes to simulate “real” road and daily operation conditions. This data is used for monitoring the energetic key performance factors by learning from the buses with the best energetic behavior. Finally, a decision-making process is applied to improve the local energy management of the less-efficient bus.

The aim of this paper is to propose an adaptive energy management strategy for a series hybrid shunter locomotive so as to reduce the vehicle operation costs. This application operation is on- demand, therefore, fuzzy logic has been employed as it has the ability to adapt and cope with the unpredictable driving profile. The results show up to 39.02 % of fuel consumption reduction by hybridising the locomotive. Moreover, up to 44.62 % and up to 55.34 %, respectively, of daily and lifecycle operational costs improvement is achieved.

Energy management in hybrid and electric vehicles is a key factor to improve the operational performance and meet the efficiency objectives defined in the transport sector. Thus, optimized energy management strategies (EMSs), before being integrated in a real system, need to be experimentally validated in test-bench platforms in order to identify the possible deviations from the expected simulation-based performance while minimizing the implementation time and field-test on the real application. An economical and flexible mean of validating these strategies is the hardware-in-the-loop simulation. Therefore, this work aims to present the design approach and comparison, by means of experimental tests, of two optimized (simulation-based) EMSs proposed for a hybrid electric bus (HEB) focusing on the real-time operational performance. Both EMSs handle the proper power split behavior of the vehicle demand among a genset (internal combustion engine + electric generator) and a hybrid energy storage system (combining Li-ion batteries with supercapacitors). The experimental platform consists of a scaled test bench emulating the electrical dc grid of a HEB. This test bench, combined with software models to control the power electronic devices, allows us to emulate the real behavior of the genset, battery, supercapacitor, traction, and auxiliary loads.

In this paper, a non-adaptive and a novel adaptive energy management strategy (EMS) are proposed for a series hybrid electric bus with a dual energy storage system (ESS) combining batteries and supercapacitors. The scenario considers the hybrid ( genset +dual ESS) and full-electric (only dual ESS) operation during the daily journey. The main goal of this paper is to highlight the improvements that can be achieved either with an enhanced adaptive EMS (fuzzy-based) compared with the non-adaptive (rule-based) approach as well as with the co-optimization of ESS sizing and operation targets in economic, fuel saving, and efficiency terms. To do so, a multi-objective optimization methodology is proposed (considering economic and ageing models) to minimize the operation cost (cycling cost) of the dual ESS and its replacements along the vehicle lifetime as well as the fuel consumption, while fulfilling the performance of the bus in its daily journey. The simulation results showed that the adaptive EMS can reduce both the operation cost of the vehicle and the average fuel consumption (up to 15% and 19%, respectively) compared with the optimal solution obtained with the non-adaptive EMS.

This paper is focused on analysing the energetic key performance indicators of a hybrid electric bus fleet in order to improve its energy management (at local and fleet level) and profitability. The analysed fleet is composed of buses with parallel and series configurations and include energy storage systems based on batteries and ultra capacitors. The test routes have been selected from a data base of urban standardised cycles. In a first stage, a dynamic programming approach has been applied to determine the initial optimal operation performance for each bus route. Then, several disruptions (e.g. traffic jams, auxiliary consumption and passenger variations) have been added to the routes to simulate "real" road and daily operation conditions. In this paper, a fleet learning methodology is proposed to analyse, process and decide based on the collected data from "real" conditions of the whole fleet. This data is used for monitoring the energetic key performance factors by learning from the buses with the best energetic behaviour. Finally, a decision making process is applied to improve the local energy management of the less-efficient bus.

Energy management in hybrid and electric vehicles is a key factor to improve the operational performance and meet the efficiency objectives defined in the transport sector. Thus, optimized energy management strategies (EMS), before being integrated in a real system, need to be validated in a scaled test-bench platform in order to identify the possible deviations from the expected simulation-based profiles, thus, saving time during the implementation in the real application. An economical and flexible way of validating these strategies is the Hardware-in-the-loop (HIL) simulation. In this framework, this work aims to compare the experimental results of two optimized (simulation-based) EMSs applied on a hybrid electric urban bus (HEB) in terms of real-time operational performance. Both EMSs handle the proper power split behavior of the vehicle demand between a genset (internal combustion engine connected to an electric generator) and a hybrid energy storage system (combining Li-ion batteries with supercapacitors). The hardware in the test-bench consist of a scaled electrical DC grid of an HEB. This hardware platform is combined with software models allowing to emulate the real behavior of the genset, battery, supercapacitor, traction and auxiliary loads.

International audience; The solar photovoltaic (PV) energy has an important place among the renewable energy sources. Therefore, several researchers have been interested by its modelling and its prediction, in order to improve the management of the electrical systems which include PV arrays. Among the existing techniques, artificial neural networks have proved their performance in the prediction of the solar radiation. However, the existing neural network models don't satisfy the requirements of certain specific situations such as the one analyzed in this paper. The aim of this research work is to supply, with electricity, a race sailboat using exclusively renewable sources. The developed solution predicts the direct solar radiation on a horizontal surface. For that, a Nonlinear Autoregressive Exogenous (NARX) neural network is used. All the specific conditions of the sailboat operation are taken into account. The results show that the best prediction performance is obtained when the training phase of the neural network is performed periodically.

The operational efficiency in a hybrid electric bus(HEB) mainly relies on the suitable design of its energy management strategy (EMS) to operate, in a proper way, the onboard energy sources. This work addresses the experimental validation of an optimized EMS (at simulation level) proposed for an urban HEB. In this case, the EMS handled the proper power split behavior of the vehicle demand among the genset(internal combustion engine connected to an electric generator) and a dual energy storage system (combining Li-ion batteries with supercapacitors). To validate the EMS, a scaled test-bench including the energy sources connected to the electrical DC grid in an HEB was build. This test-bench aims to emulate the real behavior of the genset, battery, supercapacitor, traction demand and auxiliary loads while operating in an urban route profile. The experimental results showed how the real electrical phenomena(DC voltage variations, current balancing, system losses, current ripple, auxiliary and unknown consumptions, etc.) inherent of the power-train devices may affect the energetic performance of the optimal EMS obtained in ideal conditions. Thus, based on the operational dynamics and identified issues, the optimal EMS can be tunned to improve its performance in real operation scenarios.

The aim of this paper is to compare and quantify the techno-economic implications, derived from the lifetime estimation accuracy, when a commonly used lifetime estimation method (Wholer curve based) or a more accurate one (Semiempirical method) are considered for energy storage system (ESS) sizing and management strategy definition in a transport application (hybrid electric tramway). An optimization process was carried out with each lifetime estimation method and a re evaluation process was proposed to calculate the economic error regarding the lifetime estimation accuracy. Based on the simulation results, the total operation cost (in the long term-view) of the vehicle may increase up to 13.7% when a less accurate method is applied for ESS sizing and battery lifetime estimation.

A Digital Ecosystem ( $DE$ ) is a collaborative environment, consisting of a number of heterogeneous components collaborating together on the basis of mutual interests and benefits. A MicroGrid ( $MG$ ) is an example of the $DE$ , since it is a distributed power system consisting of a number of heterogeneous components (energy sources, electrical loads, and energy storage systems) having direct/indirect impacts on each other and consequently on the entire environment. In order to provide an appropriate collaborative model, it is essential to conceive a dedicated clustering algorithm aiming at gathering the components that have mutual benefits. To do so, we propose in this paper a $DE$ cooperative model for $MG$ management, that consists of two main modules: 1) the Alliances Builder and 2) the Seller2Buyer matcher . The Alliances Builder provides an appropriate clustering algorithm, aiming at gathering all the $DE$ heterogeneous components, having similar needs and preferences. Once done, the Seller2Buyer matcher is applied inside each cluster and between clusters, targeting a better collaboration inside the $MG$ . Conducted simulations showed that the proposed algorithms yield significant results.