Multirate strong tracking extended Kalman filter and its implementation on lithium iron phosphate (LiFePO4) battery system

Jia, JB; Lin, Pengfeng; Chin, Cheng Siong; Toh, WD; Gao, Zhiqiang; Lyu, Hang; Cham, Y.T.; Mesbahi, E.

doi:10.1109/peds.2015.7203572

Cited by 15 publications

(23 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Compared to other extended Kalman filter algorithms, the strong tracking cubature extended Kalman filter (STCEKF) proposed by Gao et al [54] gave an accurate SOC prediction and faster computational time. J. Jia et al [55] proposed a multirate strong tracking extended Kalman filter (MRSTEKF) by introducing the multirate control strategy and lifting technology into a strong tracking extended Kalman filter (STEKF) to improve the tracking stability and estimation precision of SOC. Result shows that the MRSTEKF is faster than EKF and STEKF by 55.34% and 49.51%, and is more precise by 52.66% and 33.88%, respectively [55].…”

Section: Adaptive Filter Algorithmmentioning

confidence: 99%

“…J. Jia et al [55] proposed a multirate strong tracking extended Kalman filter (MRSTEKF) by introducing the multirate control strategy and lifting technology into a strong tracking extended Kalman filter (STEKF) to improve the tracking stability and estimation precision of SOC. Result shows that the MRSTEKF is faster than EKF and STEKF by 55.34% and 49.51%, and is more precise by 52.66% and 33.88%, respectively [55]. The advanced EKF method has a better performance than generality EKF, KF, and the ampere-hour counting method in terms of effectiveness and dynamic adaptability [96][97][98].…”

Section: Adaptive Filter Algorithmmentioning

confidence: 99%

See 1 more Smart Citation

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%

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

“…EEC models consist only from simple electric elements have been considered in the literature both for lead acid batteries [22,23] and for other chemistries [13,[27][28][29] as well. In addition to SOC and SOH estimations there are applications of these models as a thermal model for batteries [30].…”

Section: Model Accordance Comparisons Of Eec Models With Data For Difmentioning

confidence: 99%

On the Compatibility of Electric Equivalent Circuit Models for Enhanced Flooded Lead Acid Batteries Based on Electrochemical Impedance Spectroscopy

Aksakal

Şişman

2018

Energies

View full text Add to dashboard Cite

Electric equivalent circuit (EEC) models have been widely used to interpret the inner dynamics of all type of batteries. Added to this, they also have been used to estimate state of charge (SOC) and state of health (SOH) values in combination with different methods. Four EEC models are considered for enhanced flooded lead acid batteries (EFB) which are widely used in micro hybrid vehicles. In this study, impedance and phase prediction capabilities of models throughout a frequency spectrum from 1 mHz to 10 kHz are compared with those of experimental results to investigate their consistency with the data. The battery is charged, discharged, and aged according to appropriate standards which imitates a lifetime of a micro hybrid vehicle battery under high current partial cycling. Impedance tests are repeated between different charge and health states until the end of the battery's lifetime. It is seen that adding transmission line elements to mimic the high porous electrode electrolyte interface to a double parallel constant phase element resistance model (ZARC) can increase the model data representing capability by 100%. The mean average percentage error (MAPE) of the conventional model with respect to data is 3.2% while the same value of the transmission line added model found as 1.6%. The results can be helpful to represent an EFB in complex simulation environments, which are used in automobile industry.

show abstract

“…The state of the charge (SOC) of the battery is calculated from the coulomb counting method [26,27].…”

Section: Local Scheduling Of Bes Unitsmentioning

confidence: 99%

Distributed control scheme for residential battery energy storage units coupled with PV systems

2017

View full text Add to dashboard Cite

A distributed control method for residential battery energy storage (BES) units coupled with photovoltaic (PV) systems is presented. The objective is to utilize customer owned BES units for solving the over-voltage issues caused by high PV penetration without significantly affecting the BES owners local objectives. 24 hour ahead active power set points of the BES unit are calculated by an optimization based scheduling algorithm. The objective function is locally decided and the optimization is performed at the local level.The BES units are charged from the excess energy from the PV systems mostly during the period the grid is under risk of over-voltage. If the set points that were calculated by the optimization, turn out not to be able to maintain the voltages within the statutory limits in real time operation, the active power set points are modified. Reactive power is also utilized when active power is not sufficient. The new set points are calculated by a central controller. The performance of the proposed method is validated in a simulation study. It is shown that the residential BES units can successfully be utilized for solving over-voltage issues without significantly affecting the primary needs of the BES owners. Keywords

show abstract

Multirate strong tracking extended Kalman filter and its implementation on lithium iron phosphate (LiFePO4) battery system

Cited by 15 publications

References 11 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

On the Compatibility of Electric Equivalent Circuit Models for Enhanced Flooded Lead Acid Batteries Based on Electrochemical Impedance Spectroscopy

Distributed control scheme for residential battery energy storage units coupled with PV systems

Contact Info

Product

Resources

About