Strict Lyapunov super twisting observer design for state of charge prediction of lithium-ion batteries

The effective implementation of battery management system (BMS) in various applications such as electric vehicles (EVs), renewable energy sources (RES) integrated smart-grids and micro-grids, necessitates accurate estimation of the battery parameters and states. This paper primarily focuses on offering an improved solution to the state of charge (SOC) estimation problem of lithium-ion (Li-ion) batteries. After extensive analysis of the current state-of-the-art methods, a new strict Lyapunov super twisting algorithm (SLSTA) based approach is proposed for precise estimation of SOC under a comprehensive range of uncertainties. The error convergence and robustness of the proposed state observer are demonstrated using Lyapunov stability theory. Since the modelling parameters of the battery equivalent circuit utilised in this paper vary with various operational and external factors, a standard online method is employed for their real-time identification. The presented method is executed on a lithium-polymer (LiPo) battery with the help of a dynamic stress test (DST). The experimental results demonstrate that the proposed approach outperforms the well-known approaches in terms of accuracy, computational complexity, and convergence time.

Automated summary

This study focuses on improving the SOC estimation of Li-ion batteries, proposing a precise estimation method based on the Lyapunov super twisting algorithm and employing an online method for real-time identification of model parameters. Experimental results demonstrate that the proposed method outperforms conventional approaches in terms of accuracy, computational complexity, and convergence time.