Battery state of the charge estimation using Kalman filtering

Mastali, Mehrdad; Vazquez‐Arenas, Jorge; Fraser, Roydon; Fowler, Michael; Afshar, Sepideh; Stevens, M. B.

doi:10.1016/j.jpowsour.2013.03.131

Cited by 161 publications

(73 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…5 shows the principal block diagram of the adaptive SoC estimator based on dual EKF. In contrast to [15], [30]- [32], the proposed dual EKF system is arranged so the EKF-based state estimator can utilize either the parameters of the internal nonlinear battery model or those obtained by the second EKF, which estimates the key battery model parameters online.…”

Section: A Structure Of the Adaptive Soc Estimatormentioning

confidence: 99%

“…The dynamic model used for battery monitoring is typically nonlinear with respect to SoC and temperature [10]- [15]; thus, a nonlinear or online adaptive estimator may be required for precise SoC estimation over a wide range of battery operating conditions. In references [16] and [17], impedance spectroscopy approach has been used to identify multivariable battery models suitable for utilization within a state estimator, such as the Kalman filter [18].…”

Section: Introductionmentioning

confidence: 99%

“…1) a Luenberger estimator, which may be implemented in its basic form or extended by an integral term to improve SoC tracking ability [20]; 2) a sliding-mode observer, which results in robust estimation in the presence of model uncertainties [22]; 3) an extended Kalman filter (EKF), which is frequently used for SoC estimation [15], [23]- [25] but may also be prone to model linearization error [26]; and 4) a sigma-point Kalman filter [27], unscented Kalman filter [26], [28], or cubature Kalman filter [29], which may facilitate improved response speed and SoC tracking accuracy when compared to EKF. In the above approaches, the SoC estimator design is typically based on the parameter analytical relationships of a priori known (offline identified) battery equivalent model or parameter maps.…”

Section: Introductionmentioning

confidence: 99%

“…However, variations in aging and operating conditions (e.g., temperature) variations may affect the accuracy of the battery model within the state estimator, which may, in turn, result in pronounced SoC estimation error. To mitigate the effect of battery model parameter uncertainties to SoC estimation, state estimator may be extended with a parameter estimator utilizing sliding-mode [22] or Kalman filtering [15], [30]- [32] approach, which is then used for state estimator online adaptation. As an additional benefit, monitoring of online battery parameters may also be useful for battery SoH evaluation [14], [31], through monitoring of battery internal resistance [24] or charge capacity [33] with respect to the benchmarks based on battery-accelerated aging tests [34].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Dual EKF-Based State and Parameter Estimator for a LiFePO₄ Battery Cell

Pavković¹,

Krznar²,

Komljenović³

et al. 2017

Journal of Power Electronics

View full text Add to dashboard Cite

This work presents the design of a dual extended Kalman filter (EKF) as a state/parameter estimator suitable for adaptive state-of-charge (SoC) estimation of an automotive lithium-iron-phosphate (LiFePO 4 ) cell. The design of both estimators is based on an experimentally identified, lumped-parameter equivalent battery electrical circuit model. In the proposed estimation scheme, the parameter estimator has been used to adapt the SoC EKF-based estimator, which may be sensitive to nonlinear map errors of battery parameters. A suitable weighting scheme has also been proposed to achieve a smooth transition between the parameter estimator-based adaptation and internal model within the SoC estimator. The effectiveness of the proposed SoC and parameter estimators, as well as the combined dual estimator, has been verified through computer simulations on the developed battery model subject to New European Driving Cycle (NEDC) related operating regimes.

show abstract

Section: A Structure Of the Adaptive Soc Estimatormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Dual EKF-Based State and Parameter Estimator for a LiFePO₄ Battery Cell

Pavković¹,

Krznar²,

Komljenović³

et al. 2017

Journal of Power Electronics

View full text Add to dashboard Cite

show abstract

“…Kalman filter (KF), which is a classical state estimation approach, has been applied to estimate the SOC [16,17], and some extended Kalman filter (EKF) techniques based on nonlinear equivalent circuit modes have been employed [18,19] to improve the estimation accuracy further. The aforementioned methods can enhance the robustness and accuracy of the SOC estimation.…”

Section: Introductionmentioning

confidence: 99%

Design and Experiment of Nonlinear Observer with Adaptive Gains for Battery State of Charge Estimation

Zhao

Liu

2017

Energies

View full text Add to dashboard Cite

State of charge (SOC) is an important evaluation index for lithium-ion batteries (LIBs) in electric vehicles (EVs). This paper proposes a nonlinear observer with a new adaptive gain structure for SOC estimation based on a second-order RC model. It is able to dynamically adjust the gains and obtain a better balance between convergence speed and estimation accuracy with less computational time. A sufficient condition is derived to guarantee the uniform asymptotic stability of the observer, and its robustness with respect to disturbances and uncertainties is analyzed with the help of input-to-state stability (ISS) theory. A selection guide of the observer gains in practical application is presented. The estimation accuracy and convergence rate of the observer are evaluated and compared with those of extended Kalman filter (EKF) based on multi-temperature datasets from two different types of LIB cells. The robustness against different disturbances and uncertainties that may appear in a real vehicle is validated and discussed in detail. The experimental results show that the proposed observer is capable of achieving better performance with less computational time in comparison to EKF for different types of LIB cells under various working conditions. The observer is also capable of estimating SOC accurately for real life conditions according to the validation results of datasets from a battery management system (BMS) in an EV battery pack. Furthermore, the observer is simple enough, and is suitable for implementation on embedded hardware for LIB cells of EVs.

show abstract

Battery state of health estimation: a structured review of models, methods and commercial devices

Ungurean

Cârstoiu

Micea

et al. 2016

Int. J. Energy Res.

191

View full text Add to dashboard Cite

Summary Estimating the dynamic status parameters of a battery, such as its state of health (SoH) and remaining useful life (RUL), is still a very difficult and complex task. In this paper we perform a structured review of the most relevant state of the art models, algorithms and commercial devices employed in the estimation of the SoH/RUL battery performance figures, in the context of embedded applications. The models and estimation techniques are thoroughly classified and, for each taxonomy class, a presentation of the working principles is made. A comprehensive set of metrics is then introduced for the evaluation of the SoH/RUL estimation techniques from the perspective of their implementation and operation efficiency in embedded systems. These algorithms are then analyzed and discussed in a comparative manner, with concrete figures and results. The capability and the performance of the different types of off‐the‐shelf fuel gauges to estimate the battery SoH/RUL parameters are also evaluated in this paper. Copyright © 2016 John Wiley & Sons, Ltd.

show abstract

Battery state of the charge estimation using Kalman filtering

Cited by 161 publications

References 18 publications

Dual EKF-Based State and Parameter Estimator for a LiFePO₄ Battery Cell

Dual EKF-Based State and Parameter Estimator for a LiFePO₄ Battery Cell

Design and Experiment of Nonlinear Observer with Adaptive Gains for Battery State of Charge Estimation

Battery state of health estimation: a structured review of models, methods and commercial devices

Contact Info

Product

Resources

About

Battery state of the charge estimation using Kalman filtering

Cited by 161 publications

References 18 publications

Dual EKF-Based State and Parameter Estimator for a LiFePO4 Battery Cell

Dual EKF-Based State and Parameter Estimator for a LiFePO4 Battery Cell

Design and Experiment of Nonlinear Observer with Adaptive Gains for Battery State of Charge Estimation

Battery state of health estimation: a structured review of models, methods and commercial devices

Contact Info

Product

Resources

About

Dual EKF-Based State and Parameter Estimator for a LiFePO₄ Battery Cell

Dual EKF-Based State and Parameter Estimator for a LiFePO₄ Battery Cell