Multifunctional Estimation and Analysis of Lithium-Ion Battery State Based on Data Model Fusion under Multiple Constraints

Abstract

To overcome the problems of over-idealised estimation results, low efficiency, and insufficient reliability of traditional battery state estimation methods, this study proposes a multifunctional estimation and analysis model of battery state of charge (SOC), battery capacity, and state of power (SOP), based on data model fusion. First, the data-driven multi-scale extended Kalman filter (MDEKF) was used to de-noise the original data with random errors observed by the sensor in each cycle. The de-noised data were input to a temporal convolutional network (TCN) as training samples, and the estimation model was obtained through TCN neural network machine learning. Furthermore, a peak power estimation method based on multiple constraints was used. The accurate SOC estimation results obtained through the TCN network were used to describe and enhance the relationship between the SOC, voltage, and peak power. Therefore, the proposed method avoids the disadvantage of TCN relying excessively on the accuracy of the training data and retains the advantages of MDEKF efficiency and low cost. The experimental results show that this method can obtain accurate estimations of multi-states of battery. The dangers of over-charging and over-discharging are effectively avoided, and the safety and reliability of lithium-ion batteries are improved.