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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.

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 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.

This paper presents the key parameters of four different Series-Series Inductive Power Transmission (SS-IPT) configurations: (a) single phase with dual control, (b) single phase with primary side control, (c) three phase with dual control and (d) three phase with primary side control, considering two different control strategies; switching frequency and DC voltage regulations.

The aim of this paper is to present an alternative coil topology for ICPT (Inductively Coupled Power Transmission) links, called meander type coil, specifically for dynamic charging. The analysis is carried out using FEM software FLUX® supported by Matlab®. The goal is to design an inductive link in such a way that the coupling keeps constant while the receiver part is moving along the track, in order to make the converter and the control easier to implement. Different geometrical parameters are analyzed and compared to obtain the highest efficiency value.

International audience; MicroGrids (MGs) are one of the most useful and efficient ways to integrate the renewable energy technologies in the rural area, which has an often unused renewable resource: the organic waste. The biogas produced by the transformation of this primary energy resource can be valorised in a MG using a hybrid system based on a Solid Oxide Fuel Cell (SOFC) and a Gas Microturbine (GM) to obtain a high electrical efficiency. However, the demonstrators of this type of system are very few and the experimental data are rare. To approach the operation of a real system, the design and validation of hybrid SOFC/GM emulator have been proposed. The emulator will be then integrated into a rural MG in order to prove its contribution to the power quality and stability in the context of weak rural grids.

ISBN: 978-1-4799-2984-9; International audience; The increasing penetration of Distributed Generation systems based on Renewable Energy Sources is introducing new challenges in the current centralized electric grid. Microgrids are one of the most useful and efficient ways to integrate the renewable energy technologies. As the stability of a microgrid is highly sensitive, an energy storage system is essential and it must satisfy two criteria: to have a high storage capacity and to be able to supply fast power variations. In order to satisfy these two constraints, this paper proposes the association of a Vanadium Redox Battery (VRB) and Supercapacitor (SC) bank in a Hybrid Energy Storage System (HESS). A Three-level Neutral Point Clamped (3LNPC) Inverter is used as a unique interface between the HESS and the microgrid. The paper focuses on the dynamic modelling and validation of the HESS, the power division and the modulation strategies used with the 3LNPC to mitigate the neutral point voltage unbalance effect.

This paper presents the first results of a research project leaded by LIPSI-ESTIA concerning the association of intelligent micro sensors with advanced wind turbines (WTs) controllers to reduce the overall cost of wind energy. The sensors situated on the blades are above all used to alleviate WT fatigue loads in above rated wind speed operating area. Two multivariable gain-scheduling controllers are designed by minimizing a multiobjective H2/Hinfincriteria. One is associated to a sensor situated in the tower. The second controller is associated to sensors placed at the end of the blades. Some simulations are realized on a model of a two-bladed WT. They show that the second controller with sensors placed on the blades allows considerably alleviating WT fatigue loads.

In this paper, the implementation of an innovative inverter control strategy based on the symmetrical components is presented. Symmetrical components of the inverter output voltage and current are decomposed into dq DC quantities to control a three-phase four-leg inverter. This kind of inverter can be a good choice for the applications with hybrid power system based on renewable energy used to supply unbalanced loads. The addition of a fourth leg provides an extra degree of freedom, making possible to handle the neutral current caused by the unbalanced load. Simulation results are compared with experimental results for a prototype inverter to verify the validity of the proposed control strategy.

This paper presents the first results of a research project leaded by LIPSI-ESTIA concerning the use of intelligent micro sensors to optimize the design of Wind Turbines (WTs) controllers. The sensors situated on the blades are above all used to alleviate WT fatigue loads in above rated wind speed operating area. Two LQG controllers are designed. One is associated to a sensor situated in the tower. The second controller is associated to sensors placed at the end of the blades. Some simulations are realized on a model of a two-bladed WT. They show that the second controller with sensors placed on the blades allow considerably alleviating WT fatigue loads.

Integration of wind turbines generation into the electric power system involves application of appropriate technical standards. This document assembles some key practices from around the world on the use of wind turbines' technical regulations with a special interest in islands' power systems.