Search

The performance of lithium-ion batteries depends strongly on their ageing state; therefore, the monitoring and the prediction of the battery state of health (SoH) is necessary for an optimized and secured functioning of battery systems. This paper evaluates and compares three artificial neural network architectures for multi-step ageing prediction of lithium-ion cells: Recurrent Neural Network (RNN), Gated Recurrent Unit (GRU) and Long short-term memory (LSTM). These models use the features extracted from the cell's temperature to predict the cell's capacity. The features are extracted from experimental measurements of the cell's surface temperature and selected based on Spearman correlation analysis. The prediction results were evaluated and compared considering three different percentages of the training dataset: 60%, 70%, and 80%. Training and testing data were generated experimentally based on accelerated ageing cycling tests. During these experiments, four Nickel Manganese Cobalt/Graphite (NMC) cells were cycled under a controlled temperature environment based on a dynamic current profile extracted from the Worldwide Harmonized Light Vehicles Test Cycles. [ABSTRACT FROM AUTHOR]

This article presents an approach to optimize the electrical connection topology of tidal energy converters in a tidal farm. The methodology is based on a genetic algorithm (GA). The main purpose is to present a technique of coding to find the best electrical connection topology of the tidal farm network. The optimization model takes into account the energy loss in the submarine cables. The model gives as its output the optimal number of turbine clusters connected to each offshore substation, the number of turbines in each cluster, the cross-section of MV and HV cables, the connection design for each cluster of turbines as well as the number and the locations of the offshore substations. A particle swarm optimization algorithm (PSO) is used to confirm the results obtained with the GA. The optimization approach is applied to the Fromveur Strait (France).

The appliance of sustainable development approach in building has urged construction industry to adopt proper measurements to protect environment and reduce residential building energy consumption and CO2 emissions. Thus, an increasing interest in alternative building materials has developed including the use of bio-based materials such as cob which is studied in this paper. In the previous work, many experimental and numerical studies have been carried out to characterize thermal behaviour of earth buildings, reduce its thermal conductivity and water content. In this paper, an experimental study is carried out to determine the thermal properties and energy performance of cob building. Cob samples within different soil and fiber contents are studied using an experimental set up instrumented with flux meters and micro-thermocouples in order to evaluate the local heat flux and thermal conductivity during stationary regime. The results are analysed and compared to deduce the performant mixes in terms of thermal behaviour while respecting the French thermal regulation. A static thermal simulation based on RT 2012 calculation method (the official French calculation method for the energy performance of new residential and commercial buildings according to France thermal regulation) is used to compare energy performance between conventional and cob building using the French climate data base .