A New Cascaded Framework for Lithium-Ion Battery State and Parameter Estimation

Abstract

Lithium-ion battery on-line monitoring is challenging due to the unmeasurable characteristic of its internal states. Up to now, the most effective approach for battery monitoring is to apply advanced estimation algorithms based on equivalent circuit models. Besides, a usual method for estimating slowly varying unmeasurable parameters is to include them in the state vector with the zero-time derivative condition, which constitutes the so-called extended equivalent circuit model and has been widely used for the battery state and parameter estimation. Although various advanced estimation algorithms are applied to the joint estimation and dual estimation frameworks, the essence of these estimation frameworks has not been changed. Thus, the improvement of the battery monitoring result is limited. Therefore, a new battery monitoring structure is proposed in this paper. Firstly, thanks to the superposition principle, two sub-models are extracted. For the nonlinear one, an observability analysis is conducted. It shows that the necessary conditions for local observability depend on the battery current, the initial value of the battery capacity, and the square of the derivative of the open circuit voltage with respect to the state of charge. Then, the obtained observability analysis result becomes an important theoretical support to propose a new monitoring structure. Commonly used estimation algorithms, namely the Kalman filter, extended Kalman filter, and unscented Kalman filter, are selected and employed for it. Apart from providing a simultaneous estimation of battery open circuit voltage, more rapid and less fluctuating battery capacity estimation are the main advantages of the new proposed monitoring structure. Numerical studies using synthetic data have proven the effectiveness of the proposed framework.