Improved extended Kalman filter for state of charge estimation of battery pack

Sepasi, Saeed; Ghorbani, Reza; Liaw, Bor Yann

doi:10.1016/j.jpowsour.2013.12.093

Cited by 161 publications

(69 citation statements)

References 20 publications

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“…Properly defining and estimating SOC for Li-ion cells has therefore been the subject of considerable academic and industrial research and includes studies that employ relatively simple coulomb counting and voltage relaxation methods [35], through to more computationally demanding state estimation methods [39][40][41], typically based on the generic structure of a Kalman Filter [42,43], or observer [32,44]. A recent publication [45] attempts to revaluate the definition of SOC specifically within the context of different voltage relaxation times required for the cell to stabilise (including the practical limitations of this) and improved methods of calculating SOC within a multi-cell system.…”

Section: Case Study: Defining the State Of Charge (Soc) For A Series mentioning

confidence: 99%

A Cell-in-the-Loop Approach to Systems Modelling and Simulation of Energy Storage Systems

et al. 2015

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This research is aligned with the engineering challenge of scaling-up individual battery cells into a complete energy storage system (ESS). Manufacturing tolerances, coupled with thermal gradients and the differential electrical loading of adjacent cells, can result in significant variations in the rate of cell degradation, energy distribution and ESS performance. The uncertain transition from cell to system often manifests itself in over-engineered, non-optimal ESS designs within both the transport and energy sectors. To alleviate these issues, the authors propose a novel model-based framework for cell-in-the-loop simulation (CILS) in which a physical cell may be integrated within a complete model of an ESS and exercised against realistic electrical and thermal loads in real-time. This paper focuses on the electrical integration of both real and simulated cells within the CILS test environment. Validation of the CILS approach using real-world electric vehicle data is presented for an 18650 cell. The cell is integrated within a real-time simulation model of a series string of similar cells in a 4sp1 configuration. Results are presented that highlight the impact of cell variability (i.e., capacity and impedance) on the energy available from the multi-cell system and the useable capacity of the physical cell.

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Section: Case Study: Defining the State Of Charge (Soc) For A Series mentioning

confidence: 99%

A Cell-in-the-Loop Approach to Systems Modelling and Simulation of Energy Storage Systems

et al. 2015

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“…Here, as in many processes related to stochastic systems, the mean square error minimization is used. Therefore, the error covariance matrix related to corrected estimate is formed as follows: (21) Then (20) is inserted in (21); by substituting, X k , (22) is obtained: (22) Considering the fact that measurement noise is uncorrelated, we can write: (23) This relationship is the most complete and most general equation to correct the error covariance matrix; K k can be used to any advantage.…”

Section: Kalman Filter Algorithmmentioning

confidence: 99%

“…In this section, the filter and its associated relationships are studied [21,22].The state space model is as follows: (29) where, x(k), x(k−1) and ω(k) represent the position vector at time k and k−1, the transition matrix from time k to time k−1 and the white Gaussian noise (periodic noise), respectively. a is a common internal parameter venture between different positions in which it is constant (theoretically) a = I 2×2 .…”

Section: Extended Kalman Filtermentioning

confidence: 99%

Locating a two-wheeled robot using extended Kalman filter

Javad

Cai

2015

Teh. vjesn.

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Original scientific paper The Kalman filter is a very general method of filtering which can solve problems such as optimal estimation, prediction, noise filtering, and optimal control. A problem with detection of correct path of moving objects is the received noisy data. Therefore, it is possible that the information is incorrectly detected. There are Different methods to extract the correct data from the received information. This paper aims to detect the path of a two-wheeled robot using extended Kalman filter. For this purpose, triangular, circular, elliptical, and Sinusoidal paths were used to explore various scenarios. The results showed that the Kalman filter optimally detects the correct path with less than 3 % error rate. These results also show that error rate related to detect circular and triangular paths has the highest and lowest value, respectively, using the extended Kalman filter; in addition, the results showed that the error rate strongly depends on path changes. Keywords: path detection; the extended Kalman filter tracking; two-wheeled robot Lociranje robota s dva kotača primjenom proširenog Kalman filteraIzvorni znanstveni članak Kalman filter je vrlo općenita metoda filtriranja kojom se mogu riješiti problemi kao što su optimalna procjena, predviđanje, filtriranje buke i optimalna kontrola. Problem koji se javlja kod detekcije ispravne putanje gibajućih predmeta je prijem bučnih podataka. Stoga je moguće da se informacije neispravno pronađu. Postoje razne metode za dobivanje ispravnih podataka iz primljene obavijesti. U ovom radu cilj je otkriti putanju robota s dva kotača primjenom proširenog Kalman filtera. U tu su se svrhu rabile trokutaste, kružne, eliptične i sinusoidne putanje u istraživanju raznih scenarija. Rezultati pokazuju da se Kalmanovim filterom optimalno pronalazi ispravna putanja s manje od 3 % učestalosti pogreške. Ti rezultati također pokazuju da učestalost pogreške kod pronalaženja kružnih i trokutastih putanja ima najvišu i najnižu vrijednost primjenom Kalman filtera; uz to, rezultati su pokazali da učestalost pogreške uvelike ovisi o promjenama putanje.Ključne riječi: obilježavanje proširenim Kalman filterom; pronalaženje putanje; robot s dva kotača

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“…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

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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.

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Improved extended Kalman filter for state of charge estimation of battery pack

Cited by 161 publications

References 20 publications

A Cell-in-the-Loop Approach to Systems Modelling and Simulation of Energy Storage Systems

A Cell-in-the-Loop Approach to Systems Modelling and Simulation of Energy Storage Systems

Locating a two-wheeled robot using extended Kalman filter

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

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