Abstract
The estimation of state of charge (SOC) is a crucial evaluation index in a battery management system (BMS). The value of SOC indicates the remaining capacity of a battery, which provides a good guarantee of safety and reliability of battery operation. It is difficult to get an accurate value of the SOC, being one of the inner states. In this paper, a strong tracking cubature Kalman filter (STCKF) based on the cubature Kalman filter is presented to perform accurate and reliable SOC estimation. The STCKF algorithm can adjust gain matrix online by introducing fading factor to the state estimation covariance matrix. The typical second-order resistor-capacitor model is used as the battery’s equivalent circuit model to dynamically simulate characteristics of the battery. The exponential-function fitting method accomplishes the task of relevant parameters identification. Then, the developed STCKF algorithm has been introduced in detail and verified under different operation current profiles such as Dynamic Stress Test (DST) and New European Driving Cycle (NEDC). Making a comparison with extended Kalman filter (EKF) and CKF algorithm, the experimental results show the merits of the STCKF algorithm in SOC estimation accuracy and robustness.
Highlights
In recent years, energy-saving and emission reduction attract special attentions and environmental pollution have become more and more critical
Lithium-ion batteries conform to the demands of Electric vehicles (EVs) and hybrid electric vehicles (HEVs) for their high energy density, safety, low self-discharge and long cycle life
The state of charge (SOC) estimation error of strong tracking cubature Kalman filter (STCKF) and extended Kalman filter (EKF) method is indicated in Figure 6b with the red
Summary
Energy-saving and emission reduction attract special attentions and environmental pollution have become more and more critical. The ANN method predicts the SOC according to the nonlinear relationship between the battery SOC and its influencing factors obtained by the trained black-box battery models. This method has excellent performance if the training data is sufficient to cover the total loading conditions. A widely used method, Kalman filter (KF) [9] algorithm, which is originally developed to optimize the estimate state for linear systems, is applied to predict the battery SOC. According to [19], an adaptive observer design based on a coupled electrochemical-thermal model, is presented for simultaneous state-parameter estimation of a Li-ion cell. According to the circuit theory, the electrical behavior of the second-order RC battery model can be expressed as:
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