Online Parameter Identification and State of Charge Estimation of Battery Based on Multitimescale Adaptive Double Kalman Filter Algorithm

Wenxian Duan,Shixin Song,Chuanxue Song,Feng Xiao,Silun Peng,Yuan Chen,Yulong Shao,Baogui Xin

doi:10.1155/2020/9502605

Abstract

An accurate state of charge (SOC) can provide effective judgment for the BMS, which is conducive for prolonging battery life and protecting the working state of the entire battery pack. In this study, the first-order RC battery model is used as the research object and two parameter identification methods based on the least square method (RLS) are analyzed and discussed in detail. The simulation results show that the model parameters identified under the Federal Urban Driving Schedule (HPPC) condition are not suitable for the Federal Urban Driving Schedule (FUDS) condition. The parameters of the model are not universal through the HPPC condition. A multitimescale prediction model is also proposed to estimate the SOC of the battery. That is, the extended Kalman filter (EKF) is adopted to update the model parameters and the adaptive unscented Kalman filter (AUKF) is used to predict the battery SOC. The experimental results at different temperatures show that the EKF-AUKF method is superior to other methods. The algorithm is simulated and verified under different initial SOC errors. In the whole FUDS operating condition, the RSME of the SOC is within 1%, and that of the voltage is within 0.01 V. It indicates that the proposed algorithm can obtain accurate estimation results and has strong robustness. Moreover, the simulation results after adding noise errors to the current and voltage values reveal that the algorithm can eliminate the sensor accuracy effect to a certain extent.

Highlights

To solve the problems of the increasing global environment and the depletion of renewable energy, governments around the world advocate the new energy vehicles to replace traditional fuel vehicles
According to the parameters identified by the Hybrid Pulse Power Characteristic (HPPC) condition, a battery model is built for simulation by looking up the table; the model is recorded as extended Kalman filter (EKF)-1
According to the average parameter identified by the Federal Urban Driving Schedule (FUDS) condition, a battery model is built for simulation with fixed parameters; the model is recorded as EKF-2

Summary

Introduction

To solve the problems of the increasing global environment and the depletion of renewable energy, governments around the world advocate the new energy vehicles to replace traditional fuel vehicles. The ampere-hour integration method needs to know the accurate initial SOC value in advance, and the OCV method needs the battery to stop charging and discharging for at least two hours. Several neural networks, such as artificial neural networks [8, 9], wavelet neural networks [10], and support vector machine [11], are used to predict battery SOC. Ese methods include the long short-term memory (LSTM) network [12] and gated recurrent unit (GRU) network [13] These AI algorithms do not require an accurate battery model to obtain accurate battery SOC, these methods need numerous charge and discharge experiments under different working conditions. The accuracy of model prediction depends on the quality of experimental data

Methods

Results

Conclusion