Extended Kalman filter method for state of charge estimation of vanadium redox flow battery using thermal-dependent electrical model

Xiong, Binyu; Zhao, Jiyun; Wei, Zhongbao; Skyllas‐Kazacos, Maria

doi:10.1016/j.jpowsour.2014.03.110

Cited by 116 publications

(78 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Then, the system is applied to the Kalman filter, resulting in an extended Kalman filter (EKF) on the true nonlinear system applied for the SOC estimation [44,51,52,82,[89][90][91][92][93][94][95].…”

Section: Adaptive Filter Algorithmmentioning

confidence: 99%

State of the Art of Lithium-Ion Battery SOC Estimation for Electrical Vehicles

et al. 2018

View full text Add to dashboard Cite

Sate of charge (SOC) accurate estimation is one of the most important functions in a battery management system for battery packs used in electrical vehicles. This paper focuses on battery SOC estimation and its issues and challenges by exploring different existing estimation methodologies. The key technologies of lithium-ion battery state estimation methodologies of the electrical vehicles categorized under five groups, such as the conventional method, adaptive filter algorithm, learning algorithm, nonlinear observer, and the hybrid method, are explored in an in-depth analysis. Lithium-ion battery characteristic, battery model, estimation algorithm, and cell unbalancing are the most important factors that affect the accuracy and robustness of SOC estimation. Finally, this paper concludes with the challenges of SOC estimation and suggests other directions for possible research efforts.

show abstract

Section: Adaptive Filter Algorithmmentioning

confidence: 99%

State of the Art of Lithium-Ion Battery SOC Estimation for Electrical Vehicles

et al. 2018

View full text Add to dashboard Cite

show abstract

“…In [21][22][23][24], different effective management methods based on the parameter of the SOC of the battery were introduced, but the SOC estimation method of a VRB is not included. In the past decade, several methods have been proposed for SOC estimation in many battery applications [25][26][27][28]. A very common method for estimating the SOC of a redox flow cell is the installation of an open-circuit cell at the entry or exit ports of the electrolytes in the cell stack.…”

Section: Introductionmentioning

confidence: 99%

“…In [27], two state-of-charge monitoring methods and their use in the VRB are investigated. In [28], the SOC estimation of VRBs was realized by an extended Kalman filter (EKF) method. However, these SOC estimation methods make stack configuration more complex as one open flow cell needs to be spared for each stack and additional voltage sensors need to be installed.…”

Section: Introductionmentioning

confidence: 99%

Design of a Bidirectional Energy Storage System for a Vanadium Redox Flow Battery in a Microgrid with SOC Estimation

Gong

Lei

2017

Sustainability

View full text Add to dashboard Cite

This paper used a Vanadium Redox flow Battery (VRB) as the storage battery and designed a two-stage topology of a VRB energy storage system in which a phase-shifted full bridge dc-dc converter and three-phase inverter were used, considering the low terminal voltage of the VRB. Following this, a model of the VRB was simplified, according to the operational characteristics of the VRB in this designed topology of a VRB energy storage system (ESS). By using the simplified equivalent model of the VRB, the control parameters of the ESS were designed. For effectively estimating the state of charge (SOC) of the VRB, a traditional method for providing the SOC estimation was simplified, and a simple and effective SOC estimation method was proposed in this paper. Finally, to illustrate the proper design of the VRB ESS and the proposed SOC estimation method, a corresponding simulation was designed by Simulink. The test results have demonstrated that this proposed SOC estimation method is feasible and effective for indicating the SOC of a VRB and the proper design of this VRB ESS is very reasonable for VRB applications.

show abstract

“…Thus, modified KF algorithms have to be used in order to extend the application of KF in the nonlinear battery systems. Two commonly used types are extended Kalman filter (EKF) [15][16][17][18][19][20][21][22][23][24][25][26][27][28] and unscented Kalman filter (UKF) [29][30][31][32][33][34][35][36]. The EKF transforms a nonlinear system into a linear system by linearizing the nonlinear function on the basis of the first-order Taylor series expansion.…”

Section: Introductionmentioning

confidence: 99%

State of Charge Estimation of Lithium-Ion Batteries Using an Adaptive Cubature Kalman Filter

et al. 2015

View full text Add to dashboard Cite

Accurate state of charge (SOC) estimation is of great significance for a lithium-ion battery to ensure its safe operation and to prevent it from over-charging or over-discharging. However, it is difficult to get an accurate value of SOC since it is an inner sate of a battery cell, which cannot be directly measured. This paper presents an Adaptive Cubature Kalman filter (ACKF)-based SOC estimation algorithm for lithium-ion batteries in electric vehicles. Firstly, the lithium-ion battery is modeled using the second-order resistor-capacitor (RC) equivalent circuit and parameters of the battery model are determined by the forgetting factor least-squares method. Then, the Adaptive Cubature Kalman filter for battery SOC estimation is introduced and the estimated process is presented. Finally, two typical driving cycles, including the Dynamic Stress Test (DST) and New European Driving Cycle (NEDC) are applied to evaluate the performance of the proposed method by comparing with the traditional extended Kalman filter (EKF) and cubature Kalman filter (CKF) algorithms. Experimental results show that the ACKF algorithm has better performance in terms of SOC estimation accuracy, convergence to different initial SOC errors and robustness against voltage measurement noise as compared with the traditional EKF and CKF algorithms. OPEN ACCESSEnergies 2015, 8 5917

show abstract

Extended Kalman filter method for state of charge estimation of vanadium redox flow battery using thermal-dependent electrical model

Cited by 116 publications

References 37 publications

State of the Art of Lithium-Ion Battery SOC Estimation for Electrical Vehicles

State of the Art of Lithium-Ion Battery SOC Estimation for Electrical Vehicles

Design of a Bidirectional Energy Storage System for a Vanadium Redox Flow Battery in a Microgrid with SOC Estimation

State of Charge Estimation of Lithium-Ion Batteries Using an Adaptive Cubature Kalman Filter

Contact Info

Product

Resources

About