Measurement Techniques for Online Battery State of Health Estimation in Vehicle-to-Grid Applications

The evolution of the power grid calls for a smarter infrastructure, capable of integrating different energy sources and all the different kind of users of the grid. In addition, it should be able to dynamically adapt to fluctuations in loads and sources. In the Smart Grid scenario, energy storage systems (ESSs) play a fundamental role, allowing for decoupling production and usage times. Lithium ion batteries are among the most promising technologies in ESSs. They will allow for a full exploitation of renewable energy sources, can be used to shape load curves and constitute the energy reserve in battery electric vehicles. In addition, if such vehicles are plugged into the power grid, they can act as support electricity storage, realizing the so-called vehicle-to-grid (V2G) paradigm. In all these applications, the reliability of the battery system must be guaranteed. Therefore, measurement techniques to assess the state of health of batteries are needed. In this paper, with particular reference to V2G applications, two fast techniques to compute an index representing the state of health of a Li-ion battery are introduced. The first one relies on fuzzy logic, while the second is based on a neural network. In both cases, an early characterization of batteries in the same family of the ones to be monitored is needed. Compared with experimental data, the techniques exhibit good performances without the need for great processing power. Their performances are adequate for the intended V2G fleet management purpose.

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