Parameter identification and co-estimation of state-of-charge of Li-ion battery in real-time on Internet-of-Things platform

Abstract

An accurate estimate of the battery’s state-of-charge (SoC) is important for extending its service life and ensuring its protection. Based on a second-order equivalent circuit model, a joint estimator of online parameter identification and an improved adaptive extended Kalman filter (AEKF) for Li-ion battery SoC is developed, which tackles the problem of ill-conditioning while estimating the SoC in real-time. A three-state decoupled parameter estimation approach is designed to compute the best parameters:(i) ohmic resistance estimator with recursive least square (RLS) method, (ii) Tikhonov regularization has been introduced with the Kalman filter (KF) for the estimation of the fast time constant which improves the conditioning of the system and (iii) the measure of the slow time constant using lookup table-based method. Finally, an improved AEKF with fading weight factor mechanism is used to achieve closed-loop feedback of SOC estimation with parameter estimation, confirming the algorithm’s correctness and speed of convergence. The Chi-square test is incorporated into the AEKF to improve the rationale of the adaptive measurement covariance introduction timing. A variety of experimentally generated trending current patterns were used to validate the approach under various complex operating conditions. Furthermore, the proposed model-based diagnostic techniques have been integrated into a cloud-based system in this article, ensuring continuous and accurate battery state monitoring. All battery-related data is measured and transmitted to the cloud via the Internet of Things (IoT), where the proposed diagnostic algorithms analyze the data and estimate the SoC. As the proposed estimation approach features a low computational expense, it can be used in real-time industrial applications.