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

Abstract

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.

Improved covariance matching—electrical equivalent modeling for accurate internal state characterization of packing lithium‐ion batteries

“…To obtain the accurate mathematical expression of the battery working characteristics, different internal factors are expressed by using the proposed adaptive CM-EEC model adaptive to the cell-to-cell cascaded structures of the lithium-ion battery packs. Assuming that the voltage before the current change is expressed by U 0 and the varying voltage is described by U 1 , the ohmic resistance calculation can be realized, as shown in Equation (10).…”

“…In Equation (10), the state-space equation is conducted in practice by fully considering the working characteristics and internal composition.…”

“…Meanwhile, effective real-time state monitoring and control become crucial to ensure the safety and durability of the power supply systems. [4][5][6][7][8][9][10] In pack applications, balancing and equalization are conducted, and the balancing current is usually not measured. In this case, the current is no longer accurate when identifying the model parameters.…”

Improved Compound Correction‐Electrical Equivalent Circuit Modeling and Double Transform‐Unscented Kalman Filtering for the High‐Accuracy Closed‐Circuit voltage and State‐of‐Charge Co‐Estimation of Whole‐Life‐Cycle Lithium‐Ion batteries

“…The model parameters are nonlinear quantities and cannot be measured directly. [49][50][51] The main factors and the variation effect are introduced into the ECM to build a suitable observer.…”

“…The model parameters are nonlinear quantities and cannot be measured directly. [ 49–51 ] The main factors and the variation effect are introduced into the ECM to build a suitable observer. The internal resistance is combined with OCV and current variation to simulate the instantaneous power output capacity within a certain period.…”