Development of Fast SoH Estimation of Li-Ion Battery Pack/Modules Using Multi Series-Parallel based ANN Structure

This paper proposes an innovative state of health (SoH) estimation of the battery pack for the reused Li-Ion cells. Due to the elevated significance and interest of the overall system performance, reliability, safety and long lifetime operation, it is necessary an accurate estimation of the lithiumion batteries health status. In case of a battery pack, the degradation of each cell, will affect and result in the increase of the deterioration process of conjoint cell, and furthermore to the total degradation of the battery pack and malfunction of the normal operation. Therefore, to resolve such issue, a novel method is developed in this article, based on multi series-parallel ANN structure which will concludes with accurate and fast SoH estimation of the battery with used Li-Ion cells. Its accuracy and calculation time are improved and accelerated using an online modeling approach (during operation) with optimal generalization. By the voltage and current as the main inputs and with the help of secondary information as temperature and cycle usage, it can be possible to describe the actual quantity of energy, which is a key factor in applications. The structure of the ANN is based on multi-series parallel configuration algorithm, combining the long-short term memory (LSTM) NN features with the Convolutional neural network (CNN) abilities which enhance the ability of stable and fast estimation during all the testing period of the pack, by further reducing the computational power and increasing the generalization of the proposed paper. In this paper, the SoH estimation time is reduced to 46%, peak error and average error are reduced by 36.3% and 23.4%, comparing with the previous work of the authors. Also, it is confirmed on this study that the proposed method can be applicable not only to a battery pack, but also to single cell or multi modules, increasing its range of applications. In simple words, this study proposes an alternative and cost-effective SoH estimation and diagnosis approach for the deteriorated battery, comparing to high-cost industrial devices, focused on the low input data scenario, taking into account the inter-degradation between cells.