This paper proposes a fusion method to estimate the low frequency impedance of Lithium-Ion batteries by using excitation signals. The proposed excitation is delivered by the converter used for the interface of the battery. The fusion method is conducted in two steps. Firstly, the impedance is estimated by using a frequency method known as vector fitting. Despite its high accuracy, it has a moderate computational burden that makes it difficult to use in real-time application running on the embedded systems often used for battery and converter control. For that reason, in a second stage, recursive least squares algorithm will be used for the parameter estimation in real-time. The discussion includes not only the methods description and testing, but also the effects of the data used for the estimation (terminal current and voltage) as well as the discretization method used for the digital implementation. The proposal is validated by simulation and with preliminary experimental results.

This paper develops an optimization method for the integration of a hybrid energy storage system (HESS) considering different supercapacitors and three different forms of Li-Ion batteries: NMC (LiNiMnCo), LFP (LiFePO4) and LTO (Li2TiO3). The proposed method finds the proper demand side management (DSM) measures and optimizes the size of the HESS based on the profile selected and the electric tariff, finding the case where the net present value (NPV) is maximized. This method also evaluates which supercapacitor and battery chemistry better suit for each case. The proposed method allows to clearly evaluate not only the technical issues related with the performance of the system, but also the economical impact on the electric bill when integrating a HESS in an industrial application. This method will be also compared with the solution by only considering economical factors.