A soft-constrained unscented Kalman filter estimator for Li-ion cells electrochemical model

Marelli, Stefano; Corno, Matteo

doi:10.1109/cdc.2017.8263870

Cited by 4 publications

(4 citation statements)

References 15 publications

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“…To the best of the authors' knowledge, the field of simultaneous spatially distributed concentration and temperature estimation is still rather unexplored. This brief extends our previous contribution [17], where we estimate only the lithium concentrations disregarding the temperature. In this brief, we propose an approach to estimate both the electrochemical and thermal states.…”

Section: Introductionsupporting

confidence: 74%

Model-Based Estimation of Lithium Concentrations and Temperature in Batteries Using Soft-Constrained Dual Unscented Kalman Filtering

Marelli

Corno

2021

IEEE Trans. Contr. Syst. Technol.

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This brief proposes an electrochemical model-based estimator of the Lithium-ion (Li-ion) concentration and temperature of a Li-ion cell. The use of the electrochemical approach allows for the estimation of the spatial distribution of lithium concentration and temperature. The estimation is based on a soft-constrained dual unscented Kalman filter (DUKF) designed on the pseudo-2-D model of a Li-ion cell. The dual structure, along with parallelization, reduces the computational complexity, whereas the soft-constraint improves convergence. A simulation analysis validates the approach showing bulk state of charge (SoC) estimation error lower than 1.5%, solid-phase lithium concentration estimation errors of less than 4%, and temperature estimation errors within 0.2 • C from the true value in any point of the cell.

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Section: Introductionsupporting

confidence: 74%

Model-Based Estimation of Lithium Concentrations and Temperature in Batteries Using Soft-Constrained Dual Unscented Kalman Filtering

Marelli

Corno

2021

IEEE Trans. Contr. Syst. Technol.

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“…By examining the observability of the battery states based on the nonlinear model (11) using the method presented in [17], the model states are found weakly observable in a linear sense. An effective approach to enhance the observability is to incorporate algebraic constraints on the state variables into the estimator algorithm, so that the number of independent states can be reduced [29]. As discussed in [26], the PB-ECM-T preserves the feature of mass conservation of the P2D model.…”

Section: Extension Of Enkf To Incorporate Physical Constraintsmentioning

confidence: 99%

“…However, the implementation of EKFs requires labor-intensive derivation and computationally heavy calculation of high-dimensional Jacobian matrices of the physics-based models. To sidestep the Jacobian matrices, Tulsyan et al [28] proposed a particle filter (PF) based on a reformulated P2D model, while Marelli and Corno [29] designed a unscented Kalman filter (UKF) based on a discretized P2D model and the finite different method. Nevertheless, due to the requirement to online calculate a large number of particles/sigma points and high-dimensional covariance matrices, as well as resampling, PF and UKF are still intractable for many HBMS.…”

mentioning

confidence: 99%

“…To address this problem, nonlinear constrained Kalman filters have established, in which complex optimization problems are solved online to obtain the Kalman gain [33]. To avoid online nonlinear optimization, Strum et al [21] considered a constraint of mass conservation in the initialization step of their EKF-based estimators, and Marelli and Corno [29] proposed a soft-constrained UKF to maintain the mass conservation. Considering the drawbacks of EKF and UKF for PB-ECMs, it is of great importance to investigate how to efficiently incorporate physical constraints into the EnKF algorithm.…”

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confidence: 99%

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Constrained Ensemble Kalman Filter for Distributed Electrochemical State Estimation of Lithium-Ion Batteries

Xiong

Vilathgamuwa

et al. 2021

IEEE Trans. Ind. Inf.

101

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This paper proposes a novel model-based estimator for distributed electrochemical states of lithium-ion batteries. Through systematic simplifications of a high-order electrochemical-thermal coupled model consisting of partial differential-algebraic equations, a reduced-order battery model is obtained that features an equivalent circuit form and captures local state dynamics of interest inside the battery. Based on the physics-based equivalent circuit model, a constrained ensemble Kalman filter (EnKF) is pertinently designed to detect internal variables such as the local concentrations, overpotential, and molar flux. To address slow convergence issues due to weak observability of the battery model, the lithium ions mass conservation is judiciously considered as constraints in the estimation algorithm. The estimation performance is comprehensively examined under a wide operating range. It demonstrates that the proposed EnKFbased nonlinear estimator is able to accurately reproduce the physically-meaningful state variables at a low computational cost and is significantly superior to its prevalent benchmarks for online applications. Index Terms-Lithium-ion batteries, ensemble Kalman filter, physics-based equivalent circuit model, state estimation. I. INTRODUCTION D UE to the distinct advantages of high power and energy densities, low self-discharge rate, favorable modularity, and recent fast decline in cost, lithium-ion (Li-ion) batteries Manuscript

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High-Dimensional State Estimation Using an Adaptive Ensemble Adjustment Kalman Filter for Lithium-Ion Batteries

Xiong

Tang

et al. 2021

2021 31st Australasian Universities Power Engineering Conference (AUPEC)

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A soft-constrained unscented Kalman filter estimator for Li-ion cells electrochemical model

Cited by 4 publications

References 15 publications

Model-Based Estimation of Lithium Concentrations and Temperature in Batteries Using Soft-Constrained Dual Unscented Kalman Filtering

Model-Based Estimation of Lithium Concentrations and Temperature in Batteries Using Soft-Constrained Dual Unscented Kalman Filtering

Constrained Ensemble Kalman Filter for Distributed Electrochemical State Estimation of Lithium-Ion Batteries

High-Dimensional State Estimation Using an Adaptive Ensemble Adjustment Kalman Filter for Lithium-Ion Batteries

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