Online peak power prediction based on a parameter and state estimator for lithium-ion batteries in electric vehicles

Pei, Lei; Zhu, Chunbo; Wang, Tiansi; Lu, Rengui; Chan, C. C.

doi:10.1016/j.energy.2014.02.009

Cited by 100 publications

(48 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It can be seen that the state space equations: Equations (6) and (7) are linear. KF is a common method used for linear state estimation problems [17,18]. It comprises a set of recursive equations that are repeatedly evaluated as the system operates.…”

Section: Kf-based Internal Temperature Estimationmentioning

confidence: 99%

Online Internal Temperature Estimation for Lithium-Ion Batteries Based on Kalman Filter

et al. 2015

Self Cite

View full text Add to dashboard Cite

Abstract:The battery internal temperature estimation is important for the thermal safety in applications, because the internal temperature is hard to measure directly. In this work, an online internal temperature estimation method based on a simplified thermal model using a Kalman filter is proposed. As an improvement, the influences of entropy change and overpotential on heat generation are analyzed quantitatively. The model parameters are identified through a current pulse test. The charge/discharge experiments under different current rates are carried out on the same battery to verify the estimation results. The internal and surface temperatures are measured with thermocouples for result validation and model construction. The accuracy of the estimated result is validated with a maximum estimation error of around 1 K.

show abstract

Section: Kf-based Internal Temperature Estimationmentioning

confidence: 99%

Online Internal Temperature Estimation for Lithium-Ion Batteries Based on Kalman Filter

et al. 2015

Self Cite

View full text Add to dashboard Cite

show abstract

“…Their structures are simple and easy to calculate. These models are widely used in battery state monitoring and management applications [5,[15][16][17][18][19]. The typical equivalent circuit models include the Rint model, first order RC model, PNGV model, etc.…”

Section: Comparison Of Battery Modelsmentioning

confidence: 99%

“…In series connection, the potential of most batteries in the battery pack will not exhaust. Moreover, battery state monitoring and management, such as state of charge (SoC), state of health (SoH) estimation [2][3][4] and state of peak power (SoP) prediction [5,6], will become more and more difficult. Therefore, measures are necessary to guarantee the characteristics of the grouped batteries are as similar as possible.…”

Section: Introductionmentioning

confidence: 99%

A Novel Grouping Method for Lithium Iron Phosphate Batteries Based on a Fractional Joint Kalman Filter and a New Modified K-Means Clustering Algorithm

Song

Wei

et al. 2015

Energies

Self Cite

View full text Add to dashboard Cite

This paper presents a novel grouping method for lithium iron phosphate batteries. In this method, a simplified electrochemical impedance spectroscopy (EIS) model is utilized to describe the battery characteristics. Dynamic stress test (DST) and fractional joint Kalman filter (FJKF) are used to extract battery model parameters. In order to realize equal-number grouping of batteries, a new modified K-means clustering algorithm is proposed. Two rules are designed to equalize the numbers of elements in each group and exchange samples among groups. In this paper, the principles of battery model selection, physical meaning and identification method of model parameters, data preprocessing and equal-number clustering method for battery grouping are comprehensively described. Additionally, experiments for battery grouping and method validation are designed. This method is meaningful to application involving the grouping of fresh batteries for electric vehicles (EVs) and screening of aged batteries for recycling.

show abstract

“…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%

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

Online peak power prediction based on a parameter and state estimator for lithium-ion batteries in electric vehicles

Cited by 100 publications

References 18 publications

Online Internal Temperature Estimation for Lithium-Ion Batteries Based on Kalman Filter

Online Internal Temperature Estimation for Lithium-Ion Batteries Based on Kalman Filter

A Novel Grouping Method for Lithium Iron Phosphate Batteries Based on a Fractional Joint Kalman Filter and a New Modified K-Means Clustering Algorithm

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

Contact Info

Product

Resources

About

Online peak power prediction based on a parameter and state estimator for lithium-ion batteries in electric vehicles

Cited by 100 publications

References 18 publications

Online Internal Temperature Estimation for Lithium-Ion Batteries Based on Kalman Filter

Online Internal Temperature Estimation for Lithium-Ion Batteries Based on Kalman Filter

A Novel Grouping Method for Lithium Iron Phosphate Batteries Based on a Fractional Joint Kalman Filter and a New Modified K-Means Clustering Algorithm

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

Contact Info

Product

Resources

About

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