Lithium Polymer Battery State-of-Charge Estimation Based on Adaptive Unscented Kalman Filter and Support Vector Machine

Meng, Jinhao; Luo, Guangzhao; Gao, Fei

doi:10.1109/tpel.2015.2439578

Cited by 272 publications

(117 citation statements)

References 39 publications

(39 reference statements)

Supporting

Mentioning

117

Contrasting

Order By: Relevance

“…Kalman gain matrix : Jinhao et al [132] proposed a highly accurate algorithm for lithium polymer battery SOC estimation based on adaptive unscented Kalman filters (AUKF) and least square support vector machines (LSSVM). A summary of AUKF-LSSVM for SOC estimation is shown in Table 11.…”

Section: Others and Hybrid Algorithm Methodsmentioning

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: Others and Hybrid Algorithm Methodsmentioning

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 estimated SOC relies heavily on the look-up table, but the OCV-SOC relationship is dependent on the temperature and changes while the battery is aging. Data-driven methods based on neural networks and regression models are also applied to estimate the SOC [14,15]. The accuracy of data-driven methods relies on the features of the training samples.…”

Section: Introductionmentioning

confidence: 99%

Influence of Battery Parametric Uncertainties on the State-of-Charge Estimation of Lithium Titanate Oxide-Based Batteries

et al. 2018

Self Cite

View full text Add to dashboard Cite

State of charge (SOC) is one of the most important parameters in battery management systems, as it indicates the available battery capacity at every moment. There are numerous battery model-based methods used for SOC estimation, the accuracy of which depends on the accuracy of the model considered to describe the battery dynamics. The SOC estimation method proposed in this paper is based on an Extended Kalman Filter (EKF) and nonlinear battery model which was parameterized using extended laboratory tests performed on several 13 Ah lithium titanate oxide (LTO)-based lithium-ion batteries. The developed SOC estimation algorithm was successfully verified for a step discharge profile at five different temperatures and considering various initial SOC initialization values, showing a maximum SOC estimation error of 1.16% and a maximum voltage estimation error of 44 mV. Furthermore, by carrying out a sensitivity analysis it was showed that the SOC and voltage estimation error are only slightly dependent on the variation of the battery model parameters with the SOC.

show abstract

“…If the process noise covariance and the measurement noise covariance are too large, filter divergence will result [24]. Therefore, adaptive extended Kalman filter (AEKF) and adaptive unscented Kalman filter (AUKF) have been studied [25][26][27][28] to improve the accuracy of EKF-based SOC estimation by adaptively updating the process and measurement noise covariance.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Square Root Unscented Kalman Filter Approach for State of Charge Estimation of Lithium-Ion Batteries

2017

View full text Add to dashboard Cite

An accurate state of charge (SOC) estimation is of great importance for the battery management systems of electric vehicles. To improve the accuracy and robustness of SOC estimation, lithium-ion battery SOC is estimated using an adaptive square root unscented Kalman filter (ASRUKF) method. The square roots of the variance matrices of the SOC and noise can be calculated directly by the ASRUKF algorithm, which ensures the symmetry and nonnegative definiteness of the matrices. The process values and measurement noise covariance can be adaptively adjusted, which greatly improves the accuracy, stability, and self-adaptability of the filter. The effectiveness of the proposed method has been verified through experiments under different operating conditions. The obtained results were compared with those of extended Kalman filter (EKF) and unscented Kalman filter (UKF) , which indicates that the ASRUKF method provides better accuracy, robustness and convergence in the estimation of battery SOC for electric vehicles. The proposed method has a mean SOC estimation error of 0.5% and a maximum SOC estimation error of 0.8%. These errors are lower than those of other methods.

show abstract

Lithium Polymer Battery State-of-Charge Estimation Based on Adaptive Unscented Kalman Filter and Support Vector Machine

Cited by 272 publications

References 39 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

Influence of Battery Parametric Uncertainties on the State-of-Charge Estimation of Lithium Titanate Oxide-Based Batteries

An Adaptive Square Root Unscented Kalman Filter Approach for State of Charge Estimation of Lithium-Ion Batteries

Contact Info

Product

Resources

About