Online SoC Estimation of Lithium-Ion Batteries Using a New Sigma Points Kalman Filter

Dongdong Ge,Zhendong Zhang,Zhiping Wan,Xiangdong Kong

doi:10.3390/app112411797

Abstract

The accurate state of charge (SoC) online estimation for lithium-ion batteries is a primary concern for predicting the remaining range in electric vehicles. The Sigma points Kalman Filter is an emerging SoC filtering technology. Firstly, the charge and discharge tests of the battery were carried out using the interval static method to obtain the accurate calibration of the SoC-OCV (open circuit voltage) relationship curve. Secondly, the recursive least squares method (RLS) was combined with the dynamic stress test (DST) to identify the parameters of the second-order equivalent circuit model (ECM) and establish a non-linear state-space model of the lithium-ion battery. Thirdly, based on proportional correction sampling and symmetric sampling Sigma points, an SoC estimation method combining unscented transformation and Stirling interpolation center difference was designed. Finally, a semi-physical simulation platform was built. The Federal Urban Driving Schedule and US06 Highway Driving Schedule operating conditions were used to verify the effectiveness of the proposed estimation method in the presence of initial SoC errors and compare with the EKF (extended Kalman filter), UKF (unscented Kalman filter) and CDKF (central difference Kalman filter) algorithms. The results showed that the new algorithm could ensure an SoC error within 2% under the two working conditions and quickly converge to the reference value when the initial SoC value was inaccurate, effectively improving the initial error correction ability.

Highlights

With the rapid development of industry, the world today is facing many challenges, such as global warming, energy crisis and environmental degradation
The proposed algorithm was verified through the following steps: (1) The state of charge (SoC)-OCV relationship curve was fitted by the interval static method, as shown in Figure 2; (2) The parameters of the second-order equivalent circuit model through dynamic stress test (DST) test conditions were identified, as shown in Sections 2.2 and 2.3; (3) The MATLAB/SIMULINK block diagram of the four algorithms (UKF, central differential Kalman filter (CDKF), Extended Kalman filter (EKF), Proposed) was built; (4) The SIMULINK block diagram of the algorithm was compiled and downloaded to the ControlDesk of dSPACE through the RTI model of SIMULINK; (5) A virtual instrument for real-time estimation of lithium-ion battery SoC through
ControlDesk was built; (6) The US06 and FUDS operating conditions were run through the battery test system, the current and voltage signals were collected in real-time through the current sensor and the DS1104 board, and the output voltage, voltage difference, SoC and SoC difference were displayed through the virtual instrument built by ControlDesk

Summary

Introduction

With the rapid development of industry, the world today is facing many challenges, such as global warming, energy crisis and environmental degradation. The battery management system (BMS) is an integral component of EVs. It is used to monitor and manage battery packs to ensure the safety of EVs. The BMS can reasonably control the charge and discharge of the battery packs to increase the driving range, extend the battery life and reduce the vehicle operating cost [4,5,6,7]. Effective battery state of charge analysis, energy control management, battery fault diagnosis and safety are aspects of BMS technology that require improvement and continue to restrict the development of EVs. The accurate online estimation of the SoC of lithium-ion batteries is related to the ability to accurately predict the remaining range of an EV. Accurate online estimation of SoC is the key aspect of BMS technology [8,9]

Methods

Results

Conclusion