An Online State of Charge Estimation Algorithm for Lithium-Ion Batteries Using an Improved Adaptive Cubature Kalman Filter

Zhibing Zeng,Dong Li,Jindong Tian,Yong Tian

doi:10.3390/en11010059

Abstract

An accurate state of charge (SOC) estimation of the on-board lithium-ion battery is of paramount importance for the efficient and reliable operation of electric vehicles (EVs). Aiming to improve the accuracy and reliability of battery SOC estimation, an improved adaptive Cubature Kalman filter (ACKF) is proposed in this paper. The battery model parameters are online identified with the forgetting factor recursive least squares (FRLS) algorithm so that the accuracy of SOC estimation can be further improved. The proposed method is evaluated by two driving cycles, i.e., the New European Driving Cycle (NEDC) and the Federal Urban Driving Schedule (FUDS), and compared with the existing unscented Kalman filter (UKF) and standard CKF algorithms to verify its superiority. The experimental results reveal that comparing with the UKF and standard CKF, the improved ACKF algorithm has a faster convergence rate to different initial SOC errors with higher estimation accuracy. The root mean square error of SOC estimation without initial SOC error is less than 0.5% under both the NEDC and FUDS cycles.

Highlights

With the issues related to industrial development and environmental pollution, electric vehicles have been rapidly developed and promoted in recent years as an environmentally friendly mode of transportation
Cycle (NEDC) and Federal Urban Driving Schedule (FUDS) tests are shown in Figures 3a and 4a, 4a, respectively, and the associated voltage errors are presented in Figures 3b and 4b
Since battery model accuracy has a remarkable influence on state of charge (SOC) estimation, parameters of the Thevenin battery equivalent circuit model are updated online using the forgetting factor recursive least squares (FRLS) algorithm

Summary

Introduction

With the issues related to industrial development and environmental pollution, electric vehicles have been rapidly developed and promoted in recent years as an environmentally friendly mode of transportation. The lithium-ion battery’s features include high energy density, a long lifetime, and a low self-discharge rate, and it has been widely utilized to be the energy storage system of plug-in hybrid electric vehicles (PHEVs) and pure electric vehicles (PEVs). In order to prolong the battery’s lifetime as well as to ensure the battery operates reliability and safety, battery management systems (BMS) have to be developed to monitor and control the whole operating process of lithium-ion batteries [8]. As an essentially implicit state of the battery, the SOC cannot be directly measured by sensors. It is influenced by different types of battery materials and operating conditions [9,10]

Methods

Results

Discussion

Conclusion