Online parameters identification and state of charge estimation for lithium‐ion batteries using improved adaptive dual unscented Kalman filter

Abstract

State of charge (SOC) is a vital parameter which helps make full use of battery capacity and improve battery safety control. In this paper, an improved adaptive dual unscented Kalman filter (ADUKF) algorithm is adopted to realize coestimation of the battery model parameters and SOC. Notably, the covariance matching method that can adapt the system noise covariance and the measurement noise covariance is used to improve the estimation accuracy. Besides, singular value decomposition (SVD) is utilized to deal with the non-positive error covariance matrix in both unscented Kalman filters, further enhancing the stability of estimation algorithm. Verification results under Dynamic Stress test and Federal Urban Driving Schedule test indicate that improved ADUKF can achieve more accurate SOC estimates with error band controlled within 2.8%, while that of traditional dual unscented Kalman filter (DUKF) can only be controlled within 5%. Moreover, robustness analysis is also conducted and the validation results present that the proposed algorithm can still provide precise SOC prediction results under some disturbances, such as erroneous initial SOC, inaccurate battery capacity, and various ambient temperatures.

The optimization of state of charge and state of health estimation for lithium‐ions battery using combined deep learning and Kalman filter methods

“…Typical ECM models include the Rint model, 2 Thevenin model, PNGV model, 3 and the widely used resistance capacitance (RC) network models model. Lai et al 4 compared different RC models and found the first‐order RC (1RC) can simulate the dominant battery dynamics while keeping a low computing complexity. In contrast, the second‐order RC model improves the modeling accuracy at the expense of higher complexity.…”

“…After that, different Bayesian state estimation algorithms are applied to estimate the battery SOC and capacity concurrently. The filters used include multi‐scale dual Kalman filters, 4,7 unscented Kalman filter (UKF), 8 the PI filter, 9 H infinity filters, 10 unscented particle filters (UPF), 11 and extended Kalman filter (EKF) 12…”

An application‐oriented multistate estimation framework of lithium‐ion battery used in electric vehicles

“…Even so, the performance of this type of method is strongly depend on the quantity and quality of training data, and its estimation accuracy under different operating conditions still needs to be further verified 7 . Considering prediction performance and computation cost simultaneously, model‐based method seems more promising to be adopted in BMS to provide online SOC estimation 8‐11 . In this type of method, a sophisticated battery model is required to simulate battery dynamics primarily.…”

Online Parameters Identification and State of Charge Estimation for Lithium-Ion Battery Using Adaptive Cubature Kalman Filter

“…Among all commercially available lithium-ion batteries, the ternary lithiumion batteries are widely utilized because of their advantages of long service life, high energy density, superior performance at high and low temperatures, and environmental protection [5]. However, under certain operating conditions, the difference among all individual cells may result in battery over-charge and over-discharge, even explosions [6]. Therefore, the Battery Management System (BMS) plays a crucial role in assuring safety and monitoring the operating process [7,8].…”

“…Compared with the above-mentioned non-model-based methods, the model-based methods are more promising for EV application. The process of these approaches can be divided into three procedures: model building, identification of model parameters, and SOC estimation [6]. Many battery models have been reported to date, e.g., the electrochemical model [16], equivalent circuit models (ECMs) [17,18], and neural network models [19][20][21][22].…”