A novel adaptive dual extended Kalman filtering algorithm for the Li‐ion battery state of charge and state of health co‐estimation

Abstract

Accurate prediction of the state of health (SOH) of Li-ion battery has an important role in the estimation of battery state of charge (SOC), which can not only improve the efficiency of battery usage but also ensure its safety performance.The battery capacity will decrease with the increase of charge and discharge times, while the internal resistance will become larger, which will affect battery management. The capacity attenuation characteristics of Li-ion batteries are analyzed by aging experiment. Based on the equivalent circuit model and online parameter identification, a novel adaptive dual extended Kalman filter algorithm is proposed to consider the influence of the battery SOH on the estimation of the battery SOC, and the SOC and SOH of the Li-ion battery are estimated collaboratively. The feasibility and accuracy of the model and algorithm

A novel collaborative multiscale weighting factor‐adaptive Kalman filtering method for the time‐varying whole‐life‐cycle state of charge estimation of lithium‐ion batteries

“…The WF-AKF estimation is established by the Ah integral method and equivalent circuit modeling. The state-space description is reflected by the observation equation by considering the time-domain Kirchhoff's voltage law of the electrical ECM, as shown in Equation (13).…”

“…The battery's behavior is expressed with mathematical expressions using circuit elements, including voltage, current, resistances, capacitances, and inductances 13 . The electrical ECM is also combined with the simplified electrochemical model by introducing a concentrated structure to realize the state‐space Equation 14 .…”

“…The battery's behavior is expressed with mathematical expressions using circuit elements, including voltage, current, resistances, capacitances, and inductances. 13 The electrical ECM is also combined with the simplified electrochemical model by introducing a concentrated structure to realize the state-space Equation. 14 This modeling type includes electronic components and circuits so that the system-level design and physical modeling parameters are intuitive, according to which an improved cuckoo search particle filter is used for the SOC estimation.…”

A Critical Review of Online Battery Remaining Useful Lifetime Prediction Methods

“…Therefore, data-driven forecasting methods have become a research hotspot (Liu et al, 2021;Ren et al, 2021). According to the principles and applicable conditions of the methods used, this section refers to the methods used in many documents and divides the data-driven RUL prediction methods into artificial intelligence-based methods Ji et al (2021), filtering-based methods Xu et al (2021), and statistical data-driven methods. These methods can describe relationships and trends related to degradation based on data.…”

A novel intelligent weight decreasing firefly–particle filtering method for accurate state‐of‐charge estimation of lithium‐ion batteries

“…20 Electrochemical model is often used to describe the electrochemical reaction process inside the battery, 21 which can deeply reflect the complex electrochemical kinetic mechanism. 22 However, it is too complex, large amount of calculation, and difficult to solve online. 23 The data-driven model relies on a large number of test data to establish the relationship between input and output, 24 without considering the complex electrochemical reaction process inside the battery, 25 but a large number of tests need to spend a long time and cost.…”