Online state and parameter estimation of the Li-ion battery in a Bayesian framework

Samadi, M. Foad; Alavi, Seyed Mohammad Mahdi; Saif, Mehrdad

doi:10.1109/acc.2013.6580563

Cited by 27 publications

(26 citation statements)

References 13 publications

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“…Thef ollowing two examples of these algorithms are discussed in more detail: [17][18][19] ·t rust-region algorithm ·L evenberg-Marquardta lgorithm…”

Section: Parameter Estimationmentioning

confidence: 99%

“…This provides ar obust algorithm that converges despite having poors tart parameters in the direction of the steepest descent.I tc ombines the advantages of the steepest descent method and the Gauss-Newton method.T he difference between the two methods is that, in the trust-region method, the radius is determinedd irectly whereas,i nt he Levenberg-Marquardt method, it is determined implicitly,t hrough the use of adampingparameter. [17,18] …”

Section: Parameter Estimationmentioning

confidence: 99%

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Analysis of Lithium‐Ion Battery Models Based on Electrochemical Impedance Spectroscopy

et al. 2016

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This work is an overview of various equivalent circuits (ECs) containing various degrees of detail. The ECs are evaluated in terms of model accuracy and parameterization time for the systematic assignment of an equivalent circuit to application fields. For this purpose, impedance spectra were measured using electrochemical impedance spectroscopy at different states of charge, health and temperatures. Then the parameters of the EC were extracted using the least‐squares method and the Levenberg–Marquardt algorithm. After comparing the simulated to the measured impedance spectrum, a review and assignment of equivalent circuits for potential applications is given. Simple equivalent circuits with a series resistor and a maximum of two resistance–capacitance (RC) elements are ideal for simulations with lower dynamics. Equivalent circuits with up to five RC elements or even a constant‐phase element (CPE) are promising for simulating highly dynamic processes. By using RCPE elements the impedance spectrum can be modeled with the highest accuracy, which is why this type of model should be used for diagnostic purposes.

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“…Thef ollowing two examples of these algorithms are discussed in more detail: [17][18][19] ·t rust-region algorithm ·L evenberg-Marquardta lgorithm…”

Section: Parameter Estimationmentioning

confidence: 99%

Section: Parameter Estimationmentioning

confidence: 99%

Analysis of Lithium‐Ion Battery Models Based on Electrochemical Impedance Spectroscopy

et al. 2016

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“…(1) Initial the target vector θ0 and the covariance matrix P0; (2) For k = 1, 2, 3, …, after new measurements, zk and φk are available; (3) Update θk and Pk with the equations in Table 1; (4) Calculate the parameter values with Equation (13). …”

Section: The Parameter Identification Methodsmentioning

confidence: 99%

“…In addition, the results of these off-line identifications are always taken as reference values for on-line methods or training data for battery modelling. For the on-line methods, the resistances are always estimated on the basis of equivalent circuit models (ECMs) [5][6][7][8][9][10] or electrochemical models [11][12][13] with the utilization of the recursive optimal estimation algorithms, such as the Kalman-filter-based algorithms and the least-squares-based algorithms.…”

Section: Introductionmentioning

confidence: 99%

On-Board State-of-Health Estimation at a Wide Ambient Temperature Range in Lithium-Ion Batteries

Wang¹,

Pei²,

Wang

et al. 2015

Energies

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Abstract:A state-of-health (SOH) estimation method for electric vehicles (EVs) is presented with three main advantages: (1) it provides joint estimation of cell's aging states in terms of power and energy (i.e., SOHP and SOHE)-because the determination of SOHP and SOHE can be reduced to the estimation of the ohmic resistance increase and capacity loss, respectively, the ohmic resistance at nominal temperature will be taken as a health indicator, and the capacity loss is estimated based on a mechanistic model that is developed to describe the correlation between resistance increase and capacity loss; (2) it has wide applicability to various ambient temperatures-to eliminate the effects of temperature on the resistance, another mechanistic model about the resistance against temperature is presented, which can normalize the resistance at various temperatures to its standard value at the nominal temperature; and (3) it needs low computational efforts for on-board application-based on a linear equation of cell's dynamic behaviors, the recursive least-squares (RLS) algorithm is used for the resistance estimation. Based on the designed performance and validation experiments, respectively, the coefficients of the models are determined and the accuracy of the proposed method is verified. The results at different aging states and temperatures show good accuracy and reliability.

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“…In several literatures, results demonstrate that the dual estimators can more effectively compute the system states in the case of unknown system parameters, compared to conventional ones [25][26][27][28][29]. Also, it is proved that the original UKF has a systematic error [13,30].…”

Section: Second Form Of Sirmentioning

confidence: 99%

Kalman Randomized Joint UKF Algorithm for Dual Estimation of States and Parameters in a Nonlinear System

Safarinejadian¹,

Vafamand²

2015

Journal of Electrical Engineering and Technology

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-This article presents a new nonlinear joint (state and parameter) estimation algorithm based on fusion of Kalman filter and randomized unscented Kalman filter (UKF), called Kalman randomized joint UKF (KR-JUKF). It is assumed that the measurement equation is linear. The KR-JUKF is suitable for time varying and severe nonlinear dynamics and does not have any systematic error. Finally, joint-EKF, dual-EKF, joint-UKF and KR-JUKF are applied to a CSTR with cooling jacket, in which production of propylene glycol happens and performance of KR-JUKF is evaluated.

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Online state and parameter estimation of the Li-ion battery in a Bayesian framework

Cited by 27 publications

References 13 publications

Analysis of Lithium‐Ion Battery Models Based on Electrochemical Impedance Spectroscopy

Analysis of Lithium‐Ion Battery Models Based on Electrochemical Impedance Spectroscopy

On-Board State-of-Health Estimation at a Wide Ambient Temperature Range in Lithium-Ion Batteries

Kalman Randomized Joint UKF Algorithm for Dual Estimation of States and Parameters in a Nonlinear System

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