Particle-filtering-based failure prognosis via sigma-points: Application to Lithium-Ion battery State-of-Charge monitoring

Acuña, David E.; Orchard, Marcos E.

doi:10.1016/j.ymssp.2016.08.029

Cited by 57 publications

(27 citation statements)

References 30 publications

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“…Battery equivalent circuit model [46] and electrochemical model [47,48] in the forms of standard state space are usually selected to estimate battery SOC, while SOC is one of the state variables in these battery models. Then various state observers are adopted for online SOC estimation [49][50][51][52][53], such as Kalman filter (KF), extended Kalman filter (EKF), adaptive Kalman filter (AKF), unscented Kalman filter (UKF), slide mode observer and H1 filter. The accuracy of these modelbased approaches largely depends on the training of the battery models, the adopted state observers, and the parameter tuning such as the key parameters in model and the noise covariance matrix for KF observers.…”

Section: Soc Estimationmentioning

confidence: 99%

A brief review on key technologies in the battery management system of electric vehicles

et al. 2018

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Batteries have been widely applied in many high-power applications, such as electric vehicles (EVs) and hybrid electric vehicles, where a suitable battery management system (BMS) is vital in ensuring safe and reliable operation of batteries. This paper aims to give a brief review on several key technologies of BMS, including battery modelling, state estimation and battery charging. First, popular battery types used in EVs are surveyed, followed by the introduction of key technologies used in BMS. Various battery models, including the electric model, thermal model and coupled electro-thermal model are reviewed. Then, battery state estimations for the state of charge, state of health and internal temperature are comprehensively surveyed. Finally, several key and traditional battery charging approaches with associated optimization methods are discussed.

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Section: Soc Estimationmentioning

confidence: 99%

A brief review on key technologies in the battery management system of electric vehicles

et al. 2018

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“…e method introduced by Plett in [42][43][44] has resulted in various implementations of Kalman lter based SOC estimation for EVs. Particle lter based SOC estimation method is recently proposed for a better performance in [21][22][23][24][25].…”

Section: Complexitymentioning

confidence: 99%

Online State of Charge Estimation of Lithium-Ion Cells Using Particle Filter-Based Hybrid Filtering Approach

2020

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Filtering based state of charge (SOC) estimation with an equivalent circuit model is commonly extended to Lithium-ion (Li-ion) batteries for electric vehicle (EV) or similar energy storage applications. During the last several decades, different implementations of online parameter identification such as Kalman filters have been presented in literature. However, if the system is a moving EV during rapid acceleration or regenerative braking or when using heating or air conditioning, most of the existing works suffer from poor prediction of state and state estimation error covariance, leading to the problem of accuracy degeneracy of the algorithm. On this account, this paper presents a particle filter-based hybrid filtering method particularly for SOC estimation of Li-ion cells in EVs. A sampling importance resampling particle filter is used in combination with a standard Kalman filter and an unscented Kalman filter as a proposal distribution for the particle filter to be made much faster and more accurate. Test results show that the error on the state estimate is less than 0.8% despite additive current measurement noise with 0.05 A deviation.

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“…The objective of the first step of the methodology is to estimate the inoperability posterior density of the M system components at each time instant k given the observations y k . To do that, the particle filtering, which is a popular technique explored by several works in prognostics domain [8], [9], is used. This tool can be applied to systems with nonlinear dynamics and non-Gaussian noise.…”

Section: A Inoperability Uncertainty Estimationmentioning

confidence: 99%

“…In practice, and given the complexity of industrial systems, it is important to consider the uncertainty associated with the ToF. To do this, the notations and the new paradigms proposed in [8], [11] are used in the remainder of this paper.…”

Section: Srul Determinationmentioning

confidence: 99%

Uncertainty Quantification in System-level Prognostics: Application to Tennessee Eastman Process

Tamssaouet

Nguyen

Medjaher

et al. 2019

2019 6th International Conference on Control, Decision and Information Technologies (CoDIT)

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This paper addresses the problem of uncertainty quantification in system-level prognostics. To this purpose, a three-step methodology, based on the inoperability input-output model, is presented. The first step concerns the estimation of the system inoperability, using a new adapted particle filtering method, while considering the interactions between its components. The second step focuses on the long-term prediction of the system inoperability in order to determine its evolution. Finally, in the third step, a method for calculating the remaining useful life of the system, based on the system configuration, is formulated. The proposed methodology is applied on data obtained from the Tennessee Eastman Process simulations to predict the shutdown due to violation of process constraints.

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Particle-filtering-based failure prognosis via sigma-points: Application to Lithium-Ion battery State-of-Charge monitoring

Cited by 57 publications

References 30 publications

A brief review on key technologies in the battery management system of electric vehicles

A brief review on key technologies in the battery management system of electric vehicles

Online State of Charge Estimation of Lithium-Ion Cells Using Particle Filter-Based Hybrid Filtering Approach

Uncertainty Quantification in System-level Prognostics: Application to Tennessee Eastman Process

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