A switched SDRE filter for state of charge estimation of lithium-ion batteries. (May 2020) - Lotfi, Faraz; Ziapour, Saeedeh; Faraji, Farnoosh; Taghirad, Hamid D.
Highlights: A practical solution to precisely estimate the SOC of Li-ion batteries. A new method to tackle the model uncertainty problem and obtain a robust performance. The proposed approach is effectively applicable to other estimation issues. Ease of implementation is similar to the commonly employed Kalman filter. Abstract: Lithium-ion (Li-ion) batteries need very precise monitor of the state of charge (SOC) to ensure a long cycle life. Hence, a knowledge of the SOC is important for Li-ion batteries. Although SOC cannot be measured directly, it can be estimated from direct measurement variables based on a model of the battery. Single-Particle-Model (SPM), a reduced-order nonlinear electrochemical model, is commonly used for this purpose. State-dependent-Riccati-equation (SDRE) filter is chosen as the estimator due to its high-flexibility in handling the model's nonlinearity. However, performance of this filter is limited in presence of uncertainties. To tackle this problem, in this paper, a switching concept is induced into SDRE filter, in the form of switched estimation error covariance matrix with a certain frequency. Thus, by changing the Riccati equation dynamic in SDRE filter and proper adjustment of estimation error covariance matrix eigenvalues, performance and robustness of the common SDRE filter is significantly improved for Li-ion SOC estimation. To analyze the fidelity of such a filter in further applications, stability analysis is carried out on a class of nonlinear systems, and ultimate bound of estimation error is analytically obtained, and the influence of switching is investigated. Simulation results reveal effectiveness of the proposed filter compared to common SDRE filter, extended Kalman filter and variable structure approaches. Furthermore, experimental results verify the effectiveness of the proposed method in practice.