Evaluation on State of Charge Estimation of Batteries With Adaptive Extended Kalman Filter by Experiment Approach

Xiong, Rui; He, Haibo; Sun, Fuchun; Zhao, Kai

doi:10.1109/tvt.2012.2222684

Cited by 354 publications

(139 citation statements)

References 22 publications

Supporting

Mentioning

137

Contrasting

Unclassified

Order By: Relevance

“…Xiong et al [100] built an online AEKF algorithm with the Thevenin model to estimate SOC. This AEKF algorithm can reduce the SOC estimation error by 2%, validated by an urban dynamometer driving schedule (UDDS).…”

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

“…The extended Kalman filter (EKF) is one of the adaptive methods [13][14][15][16][17][18]. The EKF has the advantages of being closed-loop and online as well as the availability of a dynamic SOC estimation error range.…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of SOC Estimation using EECM and NST in Rechargeable LiCoO₂/LiFePO₄/LiNiMnCoO₂Cells

Lee¹,

Park²,

Kim³

2016

Journal of Electrical Engineering and Technology

View full text Add to dashboard Cite

-Lithium rechargeable cells are used in many industrial applications, because they have high energy density and high power density. For an effective use of these lithium cells, it is essential to build a reliable battery management system (BMS). Therefore, the state of charge (SOC) estimation is one of the most important techniques used in the BMS. An appropriate modeling of the battery characteristics and an accurate algorithm to correct the modeling errors in accordance with the simplified model are required for practical SOC estimation. In order to implement these issues, this approach presents the comparative analysis of the SOC estimation performance using equivalent electrical circuit modeling (EECM) and noise suppression technique (NST) in three representative LiCoO 2 /LiFePO 4 /LiNiMnCoO 2 cells extensively applied in electric vehicles (EVs), hybrid electric vehicles (HEVs) and energy storage system (ESS) applications. Depending on the difference between some EECMs according to the number of RC-ladders and NST, the SOC estimation performances based on the extended Kalman filter (EKF) algorithm are compared. Additionally, in order to increase the accuracy of the EECM of the LiFePO 4 cell, a minor loop trajectory for proper OCV parameterization is applied to the SOC estimation for the comparison of the performances among the compared to SOC estimation performance.

show abstract

“…Many recent studies have focused on SOC estimation. Methods for SOC estimation generally include voltage-based correction [2], [3], fuzzy logic-based [4]- [8], neural network [9]- [11], and Kalman filter [12]- [21] methods. Wei et al [2] proposed a method based on current time window to estimate battery SOC of fuel cells for hybrid electric cars.…”

Section: Introductionmentioning

confidence: 99%

“…Santhanagopalan et al [17], Zhang et al [18], and He et al [19] used the unscented Kalman filter in their methods. Choa et al [20], Xiong et al [21], and Sepasia et al [22] adopted an adaptive Kalman filter in their methods. The Kalman filter method is a model-based estimation technique.…”

Section: Introductionmentioning

confidence: 99%

State-of-charge Estimation for Lithium-ion Batteries Using a Multi-state Closed-loop Observer

Zhao¹,

Huang²,

Liu³

et al. 2014

Journal of Power Electronics

View full text Add to dashboard Cite

Lithium-ion batteries are widely used in hybrid and pure electric vehicles. State-of-charge (SOC) estimation is a fundamental issue in vehicle power train control and battery management systems. This study proposes a novel model-based SOC estimation method that applies closed-loop state observer theory and a comprehensive battery model. The state-space model of lithium-ion battery is developed based on a three-order resistor-capacitor equivalent circuit model. The least square algorithm is used to identify model parameters. A multi-state closed-loop state observer is designed to predict the open-circuit voltage (OCV) of a battery based on the battery state-space model. Battery SOC can then be estimated based on the corresponding relationship between battery OCV and SOC. Finally, practical driving tests that use two types of typical driving cycle are performed to verify the proposed SOC estimation method. Test results prove that the proposed estimation method is reasonably accurate and exhibits accuracy in estimating SOC within 2% under different driving cycles.

show abstract

Evaluation on State of Charge Estimation of Batteries With Adaptive Extended Kalman Filter by Experiment Approach

Cited by 354 publications

References 22 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

Comparative Analysis of SOC Estimation using EECM and NST in Rechargeable LiCoO₂/LiFePO₄/LiNiMnCoO₂Cells

State-of-charge Estimation for Lithium-ion Batteries Using a Multi-state Closed-loop Observer

Contact Info

Product

Resources

About

Evaluation on State of Charge Estimation of Batteries With Adaptive Extended Kalman Filter by Experiment Approach

Cited by 354 publications

References 22 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

Comparative Analysis of SOC Estimation using EECM and NST in Rechargeable LiCoO2/LiFePO4/LiNiMnCoO2Cells

State-of-charge Estimation for Lithium-ion Batteries Using a Multi-state Closed-loop Observer

Contact Info

Product

Resources

About

Comparative Analysis of SOC Estimation using EECM and NST in Rechargeable LiCoO₂/LiFePO₄/LiNiMnCoO₂Cells