Abstract

International audience; This paper presents a coordination mechanism for smart homes in community microgrids (smart neighborhoods) whether photovoltaics (PV), home battery storage and electric vehicles (EV) are available. The objective of the proposed method is to reduce the electricity cost of the users, as well as the aggregated peak load of the area by establishing an energy sharing ability among neighbors. A decentralized control algorithm deployed by the smart homes is used for battery control and appliance scheduling. It is assumed that the users are the owners of these resources and that they are selfish decision-makers who focus on increasing own benefit. For the neighborhood, a dynamic price model is used, where the price is associated to the aggregated consumption of the neighborhood area. Numerical results show that proposed coordination mechanism with energy sharing provides benefits for both the users and the utility.

Abstract

International audience; Engineering autonomous driving functions has become a dramatic challenge in automotive engineering since it is now required to integrate knowledge from multi-disciplinary domains. In this context, the widespread engineering methods are showing their limit since they mainly integrate technological centered point of view. Thus, these new requirements lead naturally to the design of new method for engineering in automotive field. The goal of this paper is to sketch an overview of the possible improvements that Knowledge Modeling and ontologies can bring to Systems Engineering and especially in the case of Autonomous Driving functions. Document type: Conference object

Abstract

International audience; To cope with the new transportation challenges and to ensure the safety and durability of electric vehicles and hybrid electric vehicles, high performance and reliable battery health management systems are required. The Battery State of Health (SOH) provides critical information about its performances, its lifetime and allows a better energy management in hybrid systems. Several research studies have provided different methods that estimate the battery SOH. Yet, not all these methods meet the requirement of automotive real-time applications. The real time estimation of battery SOH is important regarding battery fault diagnosis. Moreover, being able to estimate the SOH in real time ensure an accurate State of Charge and State of Power estimation for the battery, which are critical states in hybrid applications. This study provides a review of the main battery SOH estimation methods, enlightening their main advantages and pointing out their limitations in terms of real time automotive compatibility and especially hybrid electric applications. Experimental validation of an online and on-board suited SOH estimation method using model-based adaptive filtering is conducted to demonstrate its real-time feasibility and accuracy. Document type: Article